-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathInventory_optimization_model.html
More file actions
2008 lines (1960 loc) · 174 KB
/
Inventory_optimization_model.html
File metadata and controls
2008 lines (1960 loc) · 174 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Afsar Ali" />
<title>Analysis of the Proposals to reduce average level of in-process inventory</title>
<script src="data:application/x-javascript;base64,JChkb2N1bWVudCkucmVhZHkoZnVuY3Rpb24oKXsKICAgICQoJ1tkYXRhLXRvZ2dsZT0idG9vbHRpcCJdJykudG9vbHRpcCgpOwogICAgJCgnW2RhdGEtdG9nZ2xlPSJwb3BvdmVyIl0nKS5wb3BvdmVyKCk7Cn0pOwo="></script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
div.sourceCode { overflow-x: auto; }
table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
margin: 0; padding: 0; vertical-align: baseline; border: none; }
table.sourceCode { width: 100%; line-height: 100%; }
td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
td.sourceCode { padding-left: 5px; }
code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
code > span.dt { color: #902000; } /* DataType */
code > span.dv { color: #40a070; } /* DecVal */
code > span.bn { color: #40a070; } /* BaseN */
code > span.fl { color: #40a070; } /* Float */
code > span.ch { color: #4070a0; } /* Char */
code > span.st { color: #4070a0; } /* String */
code > span.co { color: #60a0b0; font-style: italic; } /* Comment */
code > span.ot { color: #007020; } /* Other */
code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
code > span.fu { color: #06287e; } /* Function */
code > span.er { color: #ff0000; font-weight: bold; } /* Error */
code > span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
code > span.cn { color: #880000; } /* Constant */
code > span.sc { color: #4070a0; } /* SpecialChar */
code > span.vs { color: #4070a0; } /* VerbatimString */
code > span.ss { color: #bb6688; } /* SpecialString */
code > span.im { } /* Import */
code > span.va { color: #19177c; } /* Variable */
code > span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code > span.op { color: #666666; } /* Operator */
code > span.bu { } /* BuiltIn */
code > span.ex { } /* Extension */
code > span.pp { color: #bc7a00; } /* Preprocessor */
code > span.at { color: #7d9029; } /* Attribute */
code > span.do { color: #ba2121; font-style: italic; } /* Documentation */
code > span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
</style>
<link href="data:text/css;charset=utf-8,%40font%2Dface%7Bfont%2Dfamily%3A%27Open%20Sans%27%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A400%3Bsrc%3Alocal%28%27Open%20Sans%27%29%2Clocal%28OpenSans%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAE8YABIAAAAAhWwAAQABAAAAAAAAAAAAAAAAAAAAAAAAAABHREVGAAABlAAAABYAAAAWABAA3UdQT1MAAAGsAAAADAAAAAwAFQAKR1NVQgAAAbgAAABZAAAAdN3O3ptPUy8yAAACFAAAAF8AAABgoT6eyWNtYXAAAAJ0AAAAmAAAAMyvDbOdY3Z0IAAAAwwAAABZAAAAog9NGKRmcGdtAAADaAAABJsAAAe0fmG2EWdhc3AAAAgEAAAAEAAAABAAFQAjZ2x5ZgAACBQAADWFAABReBn1yj5oZWFkAAA9nAAAADYAAAA293bipmhoZWEAAD3UAAAAHwAAACQNzAapaG10eAAAPfQAAAIIAAADbLTLWYhrZXJuAAA%2F%2FAAAChcAAB6Qo%2Buk42xvY2EAAEoUAAABuQAAAbz3ewp%2FbWF4cAAAS9AAAAAgAAAAIAJ2AgpuYW1lAABL8AAAAKwAAAEyFNwvSnBvc3QAAEycAAABhgAAAiiYDmoRcHJlcAAATiQAAADyAAABCUO3lqQAAQAAAAwAAAAAAAAAAgABAAAA3AABAAAAAQAAAAoACgAKAAB4AR3HNcJBAQDA8d%2BrLzDatEXOrqDd4S2ayUX1beTyDwEyyrqCbXrY%2BxPD8ylAsF0tUn%2F4nlj89Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAHgBY2Bm2cY4gYGVgYN1FqsxAwOjPIRmvsiQxviRg4mJm42NmZWFiYnlAQPTewcGhWgGBgYNBiAwdAx2ZgAK%2FP%2FLJv9PhKGFo5cpQoGBcT5IjsWDdRuQUmBgBgD40BA5AHgBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ%2FBj3QYkS1m3sZ5lQAEsHgwiDBMZGP6%2FAfEQ5D8REAnUJfxnyv%2B3%2F1r%2Fv%2Fq3Eigi8W8PA1mAA0J1MzQy3GWYwdDP0Mcwk6GDoZGRn6ELAE09H%2F8AAAB4AXVUR3fbxhPfhRqr%2F6Cr3h8pi4wpN9K9V4QEYCrq7b2F0gC1R%2BXkS3rjKWXlfJeBfaF88jH1M6TfoqNzdWaXxZ0NM7%2FftJ2ZpXfzzeVILi0uzM%2FNzkxPTU68Md64GQZ%2Bvfa6d%2BP6tatXLl%2B6eOH8uVMnTxyvVg4fGisfhNfcV0f3luz%2F7Srmc9nMyPDQ4IDFWUUgjwMcKItSmEAASaNaEcFo069WAghjFIlAegyOQaNhIEhQxALHEqIeg2P0yHLjKUuvY%2Bn1LbktrrKrOgUI%2FMUH0ebLc5Lk73yIBO4YeUrL5GGUIimuSx6mKl2tCDD8oKmCmGrkaT5Xh%2Fp6rlphaS5PYp4kPAy3Un74OjeCdTi4nFosU6Qg%2BqRBsoazczLwHdeNqpVx3AW%2BoVjdhMThOo6YkGJTl862RFq5r263bbYSHyuswVrylsSBhHzVQKDU11g6hkfAxyOf%2FDVKJ1%2FHCvgBHtNRJ%2Bb7eSYepeQ4VLZBqAeMjgM7%2FzyJJF1kuGw%2FYFpEq458Xrr65YTUa6VCEKGKVdJ%2B2FoBYYNKCwV1K6B2s1mJnPB7Ww6GtyO04ya%2FHHWPHs5P4J65NyVa5VA0E0LocwPci45b6tvMvohm1BYc1h12Xd2GrbbHVkjB1pzs6IKtOHeYd%2BJYhFasmfs9Zt%2BSZlo9pu8eg0utWZAKB8vjaxBQx7cSbK3Qdr2nBwM27vrXcUHtLolLJyJjK3CAbDcFDo3hsPZ63IH2RrsoWyskdB47jiKitFtcAgqj4wQQxN3PB81RCiCo0Y1jnUVYlOj5JHhJd2JBevIEeSQxDWzTN8PEE3AL90KtP11dVrC5II1L1w331pHFq10vPBGYeyUCFRvB7PAEzMltdubhb%2BlZ4dw9w86yyNfG%2B%2Bu0ZWOBkmsb%2BGrsrKGIN4R0XPQimnAEcj3CI6ZDR35zzHJEZlcW5cQCTMwty4umkB5B4ajHwVNhQDqdMLSAmClnhLScgYgMbQJESALUrtIvjpQz9LVxuIPSiYgQkjusZ01l4BERrPtdO9KfDErKQLne6EUbJlXHqTccNzL163tuES26ickjo5va6FIkCyIyaFEYA%2BlejuqlFxLWIYKmQG9W0tlMe0yXu80wPe%2FOavEJrd8srSFziSal30wMj5H2mH7T6H218RQ93qOFysDEgtLBoRuQUeXjyPQKexdLjoa4vtAQJiBsEXYutEo9T1%2Fm5mUdBMbXFCzIq8Z6Yl5%2B7nyic%2B1mE3xisVatpBarpcC%2FmUs9%2Fs3Csty2GRPfLMo7FrfqcS1KDxIntwVjnkEtjRJoFKEVHWmelIyxd7Y9xlqGHTSA0VfbnBks08M4W21bHczuJBrTiYixiBnsMF7PepCwTAdrGcy8UqZb5uWGvIyX9QpW0XJSrqE7hNzjjGU5u1vgRe6k5DVv4DZvpVnP6Vi0yMKLOhUvPUq9tCzvFhi5mV9KVNMvWpfRJg1bggjEml6Uz6KmiiN92dh%2BGg19OHK4TmOC61TIcAFzsF7DPNQ0fkPjNzr4sMZHaEX5fk7uLZr9LHK9AW9KF2wU%2F%2F%2FBUfaOnlREfyrK%2Frv6Hyn3ISkAAAEAAwAIAAoADQAH%2F%2F8AD3gBhXwHfFRV1vg5974yvZdMQspkSIYkQkgmhdAyIIQQWsSADCLSpajUiMgiAkuJNGmhKyJGDCyybCiyiGBHRGQtyLIuf2UX19UPy7oWyFz%2B972ZBxOE72N%2BL2%2BYd%2Bbe0%2B5p99wBAscBBIN4ACjI4D4oUJEIVAbIL8wPYX4oP1TQ3um3%2B0v5dZz2bj44nsyKLhYPXKkaL1wCAhuuXcQ69dsWyAu7qF5PBMFqQzQRkzQgYvIQCuXleXYHlCXl2x1YZg%2BF7HxMDNAQLQoVetwuKZCZjRUTQqc%2Ff7RjebisqAeuEQJXmpZUdA%2F3KgcgsJA2kL1xDNPDZqCyQAWdXiIy5YOHThUq4%2FKB1XFpgPr5heVtJuSQvJzxOeKB6HfEplzKWCEA4Sc%2BVgqkw8bwIF16K7fg0ttNJr3DajEKBqfT5UlNkwXJKyD4hCRRlFySwU%2BTvTTJkJTh1wkms6l%2FpBWa08Fmt%2FWP%2BNz2AWYcYEez3WwXvU5qECE%2FVB5ylJXl5993Hyc3zw6hkHaPoerldxVjh7eMX%2FF3hYWxu0KF382pcKpXsV%2B9QlS93Mj%2FSz%2FujinsVE1dDTszcEk1u4LpPdjXmDdw6UAsqFlUg7rmf2J%2Bd3aGLmC757GBuEe55mHNXGxifZVrLtuNNUBhwbU6wSQ5IAOyoS2MCxcH7VmpXkHIdZlFP4BPtOvFdvlZZsncL0Kl1pZcS99Iam5eK1erfhFvrkviL9HDKc5X6OV%2FChUq7aGEvw5U6QuFVCbEhOSSZHegODM7WOzxhOzZ2cVFJaXFIbfHK2cH7WlELuK3EnR5vHZJEkzvHZw35S933n0ucur5ky%2FMO7SraN2mrVuqGiNPnIt%2BNnTy6HF4fMkfvf%2B6EEjfkpWPh7rtXrJgp%2BNAk9hzQScj6194%2F%2ByxlZE72Ow0KvcdloMLbPcBiDD%2B2jdSW%2FEk6MENfk55AfQMtwabaPC0aZWZ2a6Nob1NKgxRc3qemb%2FaF0jtk3xZPtkpc4Xjr3KVXE7WDfpi%2BsfVJ1RotwUyJVFVbE4ZV3JUPi0pLsq%2B%2BXMM4A9Vd%2B%2FYcXcVvrtx7bLN61av2oINVTU11dU1NVV4cuPaFRvXrV7xDGPNH6%2BheQJpbMQaHLiz8R9fXb5w8dLl5vO7XnzhD7uef37Xxa8u%2F%2F3ipa9pxpUqrt5AYeq1b8QPxVNg5BQWw13h9k4PpEqB3Lx2eW0DlmxfqkdfUhoy9Y6EnNZgW0t7MZ%2F6smlubka%2BI0NfFckQoDwPkjih%2Bd4yrpTleTdRqoinJE6Ts7AULcTt8mRxQbYjMeLcXMpYwucgMgaCkrrMn668Z97YBwZHJm%2F%2B%2FhnWZ%2FKwOzazl5c2DerS%2Bo2Xth9eshXXd7jTu7NHHeb98%2BVHfqw%2F%2Bz%2FCmp5zhvSZe3e%2FkSOubt2EO3tExnWrrbsy%2F51x94%2BaWFa%2F84V1k%2Fbfx2Z1fWE0%2B2It%2B2zfxGEfAaBiMbBctRiug0CpIBLFUpyK2R%2BOumYgYrZB%2BcZAdoT4%2BTfM0CpsksEggGCxGoNUsV4J5sVpc5SGJE6pwxvIJgM3r97%2B1Kq1S7et2UQKUI%2Fv7znOCn%2F8jpW80ohvKaN24aOatFEFAx8XLFYDFYItR0UbkQMljuIiEgx5HMS0efW2pWtXPbVdGZb9yjruPIInv%2FsR3z%2F%2BEisAhMFkrmCRXGCB9uEUKgoomw16o95qEwxoJiaT2cDtl84CUP5G4XWJOTBmWLK8olOmNOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDFOsGAADAurFtJw%2Fbt23btm3btm3btm3btq27UCik%2F1sq1CH0I9wl%2FDTSONInsjxyKcpGc0VrRNtGx0dXRF%2FFpFiV2KbYl3j%2B%2BJz4vkTaxKjEgcSXpJzMm6yb3ALkAnoCV0ARLAcOBjdCAJQJqgWNhJZDT2EbbgTPhz8h%2BZFJyDbkFSqgVdGh6Br0BhbFFCwHVhNrj43DXuH58V74WcIkahHvyDRkLXIGeY18SxWl%2BlMHaIVuSc%2Bh3zHpmNbMJOYuy7DF2E7sFvYMJ3Clf%2B3DHecNvjm%2Fm38g1BYmioxYS5wqbhZ3S0Wl2tJkab50U04pl5CHy9vlmwqlZFJaK4uVnco55YlaUK2kNla7qEPV6epi9aMW01jN0zJohbRZ2mptj3ZWu6e91wE9vT5LX63v0c%2Fq9%2FUPRiZjprHS2GmcNG4ar8yIOcycZC4yN5mHzMvmE%2FOrhVq6NcCaYC2wNlgHrAvWQ%2Ft%2Fe6w9115r77XP2fecrE4xp65zwM3lNnZnuBfdZ17E071sXj6vrTfP2%2BHd8F74lJ%2FeL%2BHv86%2F6D%2F23Qfogf1A%2BqB10CAYGk4LFwdaf2C%2BJfQAAAAABAAAA3QCKABYAVgAFAAIAEAAvAFwAAAEOAPgAAwABeAFljgNuBEAUhr%2FajBr3AHVY27btds0L7MH3Wysz897PZIAO7mihqbWLJoahiJvpl%2BWxc4HRIm6tyrQxwkMRtzNIooj7uSDDMRE%2BCdk859Ud50z%2BTZKAPMaqyjsm%2BHDGzI37GlqiNTu%2Ftj7E00x5rrBBXDWMWdUJdMrtUveHhCfCHJOeNB4m9CK%2Bd91PWZgY37oBfov%2FiTvjKgfsss4mR5w7x5kxPZUFNtEoQ3gBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFNSzVaxFAQfhP9tprgntWkeR2PGvd1GRwqaiyhxd1bTpGXbm%2FBPdAbrFaMzy%2BT75H4YoxiYFN0UaWoDWhP2IGtZtNuNJMW0fS8E3XHLHJEiga66lFTq0cNtR5dXhLRpSbXJTpJB5U00XSrgOqEGqjqwvxA9GsekiJBw2KIekUPdQCSJZAQ86hE8QMVxDoqhgKMQDDaZ6csYH9Msxic9YIOVXgLK2XO01WzXkrLSGFTwp10yq05WdyQxp1ktLG5FgK8rF8%2FP7PpkbQcLa%2FJ2Mh6Wu42D2sk7GXT657H%2BY7nH%2FNW%2BNzz%2Bf9ov%2F07DXE7QQYAAA%3D%3D%29%20format%28%22woff%22%29%7D%40font%2Dface%7Bfont%2Dfamily%3A%27Open%20Sans%27%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A700%3Bsrc%3Alocal%28%27Open%20Sans%20Bold%27%29%2Clocal%28OpenSans%2DBold%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFIkABIAAAAAjFQAAQABAAAAAAAAAAAAAAAAAAAAAAAAAABHREVGAAABlAAAABYAAAAWABAA3UdQT1MAAAGsAAAADAAAAAwAFQAKR1NVQgAAAbgAAABZAAAAdN3O3ptPUy8yAAACFAAAAGAAAABgonWhGGNtYXAAAAJ0AAAAmAAAAMyvDbOdY3Z0IAAAAwwAAABdAAAAqhMtGpRmcGdtAAADbAAABKQAAAfgu3OkdWdhc3AAAAgQAAAADAAAAAwACAAbZ2x5ZgAACBwAADiOAABYHAyUF61oZWFkAABArAAAADYAAAA29%2BHHDmhoZWEAAEDkAAAAHwAAACQOKQeIaG10eAAAQQQAAAICAAADbOuUTaVrZXJuAABDCAAAChcAAB6Qo%2Buk42xvY2EAAE0gAAABugAAAbyyH8b%2FbWF4cAAATtwAAAAgAAAAIAJoAh9uYW1lAABO%2FAAAALcAAAFcGJAzWHBvc3QAAE%2B0AAABhgAAAiiYDmoRcHJlcAAAUTwAAADnAAAA%2BMgJ%2FGsAAQAAAAwAAAAAAAAAAgABAAAA3AABAAAAAQAAAAoACgAKAAB4AR3HNcJBAQDA8d%2BrLzDatEXOrqDd4S2ayUX1beTyDwEyyrqCbXrY%2BxPD8ylAsF0tUn%2F4nlj89Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae%2Fp45np2e356gbeH94HP8Q3x3feH%2FX38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH%2F0UqxTrEVsbux%2FPHe8b3xh%2FlgglzESJRJfE6MS6ZChZJzkj%2BRouCA9GJKQuMhI5hsZRHR2A7kZ%2FYZWxldhtPDPeFd%2BIPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ%2B8BX5Lvxe%2FqOACmWEnsJ60SsyYjqxiLhE3CoeE6%2BLL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr%2BdXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10%2FpiU3YZu6%2B91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE%2BAmeAW%2Bp8R%2FA5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL%2FaDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8%2BXI%2BqI1COZ%2FkliIXhPkiyDo3vCnG2CaEn0%2B2lH%2BgmfIvotowZa3769ULZST4K%2BcujqTb%2Fj36S4w%2FQmgDF0tWvalemNWLX%2BKSMBvYkhQSLG2FZR%2BafmERIsqPpn7%2ByvxjfMlsTjlihz3OuZE38bTtlAAa%2FTAFAHgBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW%2BQwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT%2FfEXNRJwqmlaemnQMbN8E1OE7Mzb%2FP%2F8xzKZrEMA2hl3rQATa0Uxs2bN%2B2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq%2F19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6%2Fenoi05EcG62tsXhkPcdk87BHVC%2BZXleUPrOsUHaUI2tb4y%2F8OwbsTEAJAA%3D%3D%29%20format%28%22woff%22%29%7D%2A%7Bbox%2Dsizing%3Aborder%2Dbox%7Dbody%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dfamily%3A%22Open%20Sans%22%2C%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bcolor%3A%23606c71%7Da%7Bcolor%3A%231e6bb8%3Btext%2Ddecoration%3Anone%7Da%3Ahover%7Btext%2Ddecoration%3Aunderline%7D%2Epage%2Dheader%7Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23159957%3Bbackground%2Dimage%3Alinear%2Dgradient%28120deg%2C%23155799%2C%23159957%29%3Bpadding%3A1%2E5rem%202rem%7D%2Eproject%2Dname%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A%2E1rem%3Bfont%2Dsize%3A2rem%7D%2Eproject%2Dtagline%7Bmargin%2Dbottom%3A2rem%3Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E5rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E2rem%7D%40media%20screen%20and%20%28max%2Dwidth%3A%2042em%29%7B%2Epage%2Dheader%7Bpadding%3A1rem%7D%2Eproject%2Dname%7Bfont%2Dsize%3A1%2E75rem%7D%2Eproject%2Dtagline%7Bfont%2Dsize%3A1%2E2rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dsize%3A1rem%7D%7D%2Emain%2Dcontent%3Afirst%2Dchild%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20img%7Bmax%2Dwidth%3A100%25%7D%2Emain%2Dcontent%20h1%2C%2Emain%2Dcontent%20h2%2C%2Emain%2Dcontent%20h3%2C%2Emain%2Dcontent%20h4%2C%2Emain%2Dcontent%20h5%2C%2Emain%2Dcontent%20h6%7Bmargin%2Dtop%3A2rem%3Bmargin%2Dbottom%3A1rem%3Bfont%2Dweight%3A400%3Bcolor%3A%23159957%7D%2Emain%2Dcontent%20p%7Bmargin%2Dbottom%3A1em%7D%2Emain%2Dcontent%20code%7Bpadding%3A2px%204px%3Bfont%2Dfamily%3AConsolas%2C%22Liberation%20Mono%22%2CMenlo%2CCourier%2Cmonospace%3Bcolor%3A%23383e41%3Bbackground%2Dcolor%3A%23f3f6fa%3Bborder%2Dradius%3A%2E3rem%7D%2Emain%2Dcontent%20pre%7Bpadding%3A%2E8rem%3Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A1rem%3Bfont%3A1rem%20Consolas%2C%22Liberation%20Mono%22%2CMenlo%2CCourier%2Cmonospace%3Bcolor%3A%23567482%3Bword%2Dwrap%3Anormal%3Bbackground%2Dcolor%3A%23f3f6fa%3Bborder%3Asolid%201px%20%23dce6f0%3Bborder%2Dradius%3A%2E3rem%3Bline%2Dheight%3A1%2E45%3Boverflow%3Aauto%7D%2Emain%2Dcontent%20pre%3E%20code%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dsize%3A1rem%3Bcolor%3A%23567482%3Bword%2Dbreak%3Anormal%3Bwhite%2Dspace%3Apre%3Bbackground%3Atransparent%3Bborder%3A0%7D%2Emain%2Dcontent%20pre%20code%2C%2Emain%2Dcontent%20pre%20tt%7Bdisplay%3Ainline%3Bpadding%3A0%3Bline%2Dheight%3Ainherit%3Bword%2Dwrap%3Anormal%3Bbackground%2Dcolor%3Atransparent%3Bborder%3A0%7D%2Emain%2Dcontent%20pre%20code%3Abefore%2C%2Emain%2Dcontent%20pre%20code%3Aafter%2C%2Emain%2Dcontent%20pre%20tt%3Abefore%2C%2Emain%2Dcontent%20pre%20tt%3Aafter%7Bcontent%3Anormal%7D%2Emain%2Dcontent%20ul%2C%2Emain%2Dcontent%20ol%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20blockquote%7Bpadding%3A0%201rem%3Bmargin%2Dleft%3A0%3Bfont%2Dsize%3A1%2E2rem%3Bcolor%3A%23819198%3Bborder%2Dleft%3A%2E3rem%20solid%20%23dce6f0%7D%2Emain%2Dcontent%20blockquote%3E%3Afirst%2Dchild%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20blockquote%3E%3Alast%2Dchild%7Bmargin%2Dbottom%3A0%7D%2Emain%2Dcontent%20table%7Bwidth%3A100%25%3Boverflow%3Aauto%3Bword%2Dbreak%3Anormal%3Bword%2Dbreak%3Akeep%2Dall%3Bborder%2Dcollapse%3Acollapse%3Bborder%2Dspacing%3A0%3Bmargin%3A1rem%200%7D%2Emain%2Dcontent%20table%20th%7Bfont%2Dweight%3A700%3Bbackground%2Dcolor%3A%234CAF50%3Bcolor%3A%23fff%7D%2Emain%2Dcontent%20table%20th%2C%2Emain%2Dcontent%20table%20td%7Bpadding%3A%2E5rem%201rem%3Bborder%2Dbottom%3A1px%20solid%20%23e9ebec%3Btext%2Dalign%3Aleft%7D%2Emain%2Dcontent%20table%20tr%3Anth%2Dchild%28odd%29%7Bbackground%2Dcolor%3A%23f2f2f2%7D%2Emain%2Dcontent%20dl%7Bpadding%3A0%7D%2Emain%2Dcontent%20dl%20dt%7Bpadding%3A0%3Bmargin%2Dtop%3A1rem%3Bfont%2Dsize%3A1rem%3Bfont%2Dweight%3A700%7D%2Emain%2Dcontent%20dl%20dd%7Bpadding%3A0%3Bmargin%2Dbottom%3A1rem%7D%2Emain%2Dcontent%20hr%7Bmargin%3A1rem%200%3Bborder%3A0%3Bheight%3A1px%3Bbackground%3A%23aaa%3Bbackground%2Dimage%3Alinear%2Dgradient%28to%20right%2C%23eee%2C%23aaa%2C%23eee%29%7D%2Emain%2Dcontent%2C%2Etoc%7Bmax%2Dwidth%3A64rem%3Bpadding%3A2rem%204rem%3Bmargin%3A0%20auto%3Bfont%2Dsize%3A1%2E1rem%7D%2Etoc%7Bpadding%2Dbottom%3A0%7D%2Etoc%20ul%7Bmargin%2Dbottom%3A0%7D%40media%20screen%20and%20%28min%2Dwidth%3A%2042em%29%20and%20%28max%2Dwidth%3A%2064em%29%7B%2Etoc%7Bpadding%3A2rem%202rem%200%7D%2Emain%2Dcontent%7Bpadding%3A2rem%7D%7D%40media%20screen%20and%20%28max%2Dwidth%3A%2042em%29%7B%2Etoc%7Bpadding%3A2rem%201rem%200%3Bfont%2Dsize%3A1rem%7D%2Emain%2Dcontent%7Bpadding%3A2rem%201rem%3Bfont%2Dsize%3A1rem%7D%2Emain%2Dcontent%20pre%2C%2Emain%2Dcontent%20pre%3E%20code%7Bfont%2Dsize%3A%2E9rem%7D%2Emain%2Dcontent%20blockquote%7Bfont%2Dsize%3A1%2E1rem%7D%7D%2Esite%2Dfooter%7Bpadding%2Dtop%3A2rem%3Bmargin%2Dtop%3A2rem%3Bborder%2Dtop%3Asolid%201px%20%23eff0f1%3Bfont%2Dsize%3A1rem%7D%2Esite%2Dfooter%2Downer%7Bdisplay%3Ablock%3Bfont%2Dweight%3A700%7D%2Esite%2Dfooter%2Dcredits%7Bcolor%3A%23819198%7D%0Acode%20%3E%20span%2Ekw%20%7B%20color%3A%20%23a71d5d%3B%20font%2Dweight%3A%20normal%3B%20%7D%20%0Acode%20%3E%20span%2Edt%20%7B%20color%3A%20%23795da3%3B%20%7D%20%0Acode%20%3E%20span%2Edv%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Ebn%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Efl%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Ech%20%7B%20color%3A%20%234070a0%3B%20%7D%20%0Acode%20%3E%20span%2Est%20%7B%20color%3A%20%23183691%3B%20%7D%20%0Acode%20%3E%20span%2Eco%20%7B%20color%3A%20%23969896%3B%20font%2Dstyle%3A%20italic%3B%20%7D%20%0Acode%20%3E%20span%2Eot%20%7B%20color%3A%20%23007020%3B%20%7D%20%0A" rel="stylesheet" type="text/css" />
</head>
<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">Analysis of the Proposals to reduce average level of in-process inventory</h1>
<h4 class="author project-author">Afsar Ali</h4>
</section>
<div id="TOC" class="toc">
<ul>
<li><a href="#executive-summary">Executive Summary</a><ul>
<li><a href="#current-status-report">Current Status Report</a></li>
<li><a href="#proposed-decreasing-print-time">Proposed: Decreasing print time</a></li>
<li><a href="#proposed-increasing-print-time">Proposed: Increasing print time</a></li>
<li><a href="#proposed-experienced-inspector">Proposed: Experienced inspector</a></li>
</ul></li>
<li><a href="#recommended-proposal-2-inspector-8-presses-at-increased-rate">Recommended Proposal: 2 Inspector, 8 Presses at increased rate</a></li>
<li><a href="#relevant-final-thoughts">Relevant Final Thoughts</a></li>
</ul>
</div>
<section class="main-content">
<div id="to-seymore-butts" class="section level5">
<h5><strong>To:</strong> Seymore Butts</h5>
</div>
<div id="from-afsar-ali" class="section level5">
<h5><strong>From:</strong> Afsar Ali</h5>
</div>
<div id="date-september-21-2018" class="section level5">
<h5><strong>Date:</strong> September 21, 2018</h5>
</div>
<div id="re-analysis-of-the-proposals-to-reduce-average-level-of-in-process-inventory" class="section level5">
<h5><strong>RE:</strong> Analysis of the Proposals to reduce average level of in-process inventory</h5>
<hr />
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Title: Artie's Dream</span>
<span class="co"># Purpose: Final Project </span>
<span class="co"># Date: May 31, 2018</span>
<span class="co"># Author: Afsar Ali</span>
<span class="co"># Clear packages </span>
<span class="cf">if</span>(<span class="kw">is.null</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs) <span class="op">==</span><span class="st"> </span><span class="ot">FALSE</span>)<span class="kw">lapply</span>(
<span class="kw">paste</span>(<span class="st">"package:"</span>, <span class="kw">names</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs), <span class="dt">sep=</span><span class="st">""</span>),
detach, <span class="dt">character.only =</span> <span class="ot">TRUE</span>, <span class="dt">unload =</span> <span class="ot">TRUE</span>)
<span class="co"># Clear environment</span>
<span class="kw">rm</span>(<span class="dt">list =</span> <span class="kw">ls</span>(<span class="dt">all =</span> <span class="ot">TRUE</span>))
<span class="co"># Load packages</span>
<span class="kw">library</span>(tidyverse)
<span class="kw">library</span>(queueing)
<span class="kw">library</span>(kableExtra)
<span class="kw">library</span>(formattable)</code></pre></div>
</div>
<div id="executive-summary" class="section level2">
<h2>Executive Summary</h2>
<p>How can we reduce the average number of poster sheets waiting to complete inspection? I was tasked with analyzing and producing solutions to reduce the backlog by taking into account the cost associated with each task and cost of in-process inventory. According to my findings, we should only use 8 printing press, increase the print time by 15 mins and hire an additional inspector. This will reduce our <strong>Total cost by $50.63 per hour</strong> from current the total cost of $203.14 per hour to $152.51 per hour and optimizes our overall operation. With the task of analyzing the two initial proposals we discussed, I was able to create a third solution that maximizes the poster sheet flow while minimizing our cost.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Current Status:</span>
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the cost of the power for running each press $7.00</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f)<span class="op">+</span><span class="dv">1</span> <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 8.00 11.45 4.45 98.12 38.12 56.00 35.58 91.58 91.58 147.58
## B 9.00 8.35 1.35 71.55 11.55 63.00 10.78 66.78 73.78 129.78
## C 10.00 7.52 0.52 64.43 4.43 70.00 4.14 60.14 74.14 130.14
## D 11.00 7.21 0.21 61.82 1.82 77.00 1.70 57.70 78.70 134.70
## E 12.00 7.09 0.09 60.75 0.75 84.00 0.70 56.70 84.70 140.70</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 139.78 202.78
## [2,] 109.44 172.44</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">3</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">3</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 140.14 203.14
## [2,] 109.80 172.80</code></pre>
<div id="current-status-report" class="section level3">
<h3>Current Status Report</h3>
<p>Table 1 breaks down our status quo. Our current <strong>total cost is $203.14 per hour</strong> (highlighted green). There is an average of 7 to 8 posters in Printing Press and 7 posters in the Inspection Station, which we can call work-in-process inventory (WIP). The inventory waiting in the queue (WIQ) for service is an average of 0 to 1 posters at Printing Press and 6 posters at the Inspection Station. The wait time of the inventory in the queue for service is in average 4 minutes 25 seconds at Printing Press and 52 minutes 30 seconds in the Inspection Station. We are already aware that there is a backlog at the Inspection Station. Excluding the Cost of servers, the result shows that the main impact on in-process inventory cost comes from the Inspection Station $49 per hour for inventory waiting to be serviced (highlighted red).</p>
<p><em>Table 1: Currently the Total Cost is $203.14 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">now <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">3</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(now) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(now)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
now<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(now)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>, <span class="dt">escape =</span> F) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now)<span class="op">-</span><span class="dv">3</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) </code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
56.93
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of servers
</td>
<td style="text-align:right;">
70.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
87.00
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: darkred;">
Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
4.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
53.14
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of all in-process inventory (WIP) ($ Per Hour)
</td>
<td style="text-align:right;">
60.14
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
116.14
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;">
74.14
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
140.14
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: green;">
Total Cost ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
130.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
203.14
</td>
</tr>
</tbody>
</table>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">##Proposal 4: Take slightly longer to make the prints (which would increase their average time to make a print to 1.2 hours), so that the inspector can keep up with his output better. This also would reduce the cost of the power for running each press from $7.00 to $6.50 per hour. (By contrast, decreasing the time would increase this cost to $7.50 per hour while decreasing the average time to make a print to 0.8 hour.):
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span><span class="op">/</span><span class="fl">1.2</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour; increase their average time to make a print to 1.2 hours</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="fl">6.5</span> <span class="co">#the cost of the power for running each press $7.00 to $6.50 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 9.00 19.36 10.96 165.94 93.94 58.50 87.68 154.88 146.18 213.38
## B 10.00 11.05 2.65 94.69 22.69 65.00 21.18 88.38 86.18 153.38
## C 11.00 9.41 1.01 80.62 8.62 71.50 8.05 75.25 79.55 146.75
## D 12.00 8.83 0.43 75.68 3.68 78.00 3.43 70.63 81.43 148.63</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 212.18 286.38
## [2,] 181.84 256.04</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 152.18 226.38
## [2,] 121.84 196.04</code></pre>
</div>
<div id="proposed-decreasing-print-time" class="section level3">
<h3>Proposed: Decreasing print time</h3>
<p>If we were to take slightly longer time to make the prints, so that the inspector can keep up with the output and reduce the cost of the power for running each press from $7.00 to $6.50 per hour, the <strong>total cost would increase by $23.24 per hour</strong>, from current total cost of $203.14 per hour to $226.38 per hour (highlighted green in table 2). This increase is due to the cost of in-process inventory, by decreasing the print time the average WIP inventory goes from 7-8 posters to about 11 posters in printing press. Although the cost of printing press gets reduced by $5, the result shows that cost of in-process inventory WIQ increases from $4.14 per hours to $21.18 per hour (highlighted red in table 2).</p>
<p><em>Table 2: Decreasing print time by 10 mins would increase Total Cost by $23.24 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p4a <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">2</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(p4a) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(p4a)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
p4a<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(p4a)
now1 <-<span class="st"> </span><span class="kw">cbind</span>(now, p4a)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now1 <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now1), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now1)<span class="op">-</span><span class="dv">3</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">7</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">add_header_above</span>(<span class="kw">c</span>(<span class="st">" "</span>, <span class="st">"Current"</span> =<span class="st"> </span><span class="dv">3</span>, <span class="st">"Proposed: Decreasing print time"</span> =<span class="st"> </span><span class="dv">3</span>))</code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="border-bottom:hidden" colspan="1">
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Current
</div>
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Proposed: Decreasing print time
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
<td style="text-align:right;">
11.05
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
18.05
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
<td style="text-align:right;">
2.65
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
8.77
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
<td style="text-align:right;">
94.69
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
154.69
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
56.93
</td>
<td style="text-align:right;">
22.69
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">
75.19
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of servers
</td>
<td style="text-align:right;">
70.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
87.00
</td>
<td style="text-align:right;">
65.00
</td>
<td style="text-align:right;">
17.00
</td>
<td style="text-align:right;font-weight: bold;">
82.00
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: darkred;">
Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
4.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
53.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
21.18
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;">
49.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: darkred;font-weight: bold;">
70.18
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of all in-process inventory (WIP) ($ Per Hour)
</td>
<td style="text-align:right;">
60.14
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
116.14
</td>
<td style="text-align:right;">
88.38
</td>
<td style="text-align:right;">
56.00
</td>
<td style="text-align:right;font-weight: bold;">
144.38
</td>
</tr>
<tr>
<td style="text-align:left;">
Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)
</td>
<td style="text-align:right;">
74.14
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
140.14
</td>
<td style="text-align:right;">
86.18
</td>
<td style="text-align:right;">
66.00
</td>
<td style="text-align:right;font-weight: bold;">
152.18
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;color: white;background-color: green;">
Total Cost ($ Per Hour)
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
130.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
203.14
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
153.38
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;">
73.00
</td>
<td style="text-align:right;font-weight: bold;color: white;background-color: green;font-weight: bold;">
226.38
</td>
</tr>
</tbody>
</table>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">##Proposal 4: Take slightly longer to make the prints (which would increase their average time to make a print to 1.2 hours), so that the inspector can keep up with his output better. This also would reduce the cost of the power for running each press from $7.00 to $6.50 per hour. (By contrast, decreasing the time would increase this cost to $7.50 per hour while decreasing the average time to make a print to 0.8 hour.):
<span class="co"># Evaluating the status quo print reproductions of the paintings and photographs</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co"># The poster sheets arrive randomly to the group of presses at a mean rate of 7 per hour </span>
mu_f <-<span class="st"> </span><span class="dv">1</span><span class="op">/</span>.<span class="dv">8</span> <span class="co">#The time required to make a print has an exponential distribution with a mean of 1 hour; decrease their average time to make a print to .8 hours</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="fl">7.5</span> <span class="co">#the cost of the power for running each press $7.00 to $7.50 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">12</span> <span class="co"># Max number of servers</span>
p4_p <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system</span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_p <-<span class="st"> </span><span class="kw">rbind</span>(p4_p, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_p) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_p)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 6.00 17.12 11.52 146.73 98.73 45.00 92.15 136.95 137.15 181.95
## B 7.00 7.54 1.94 64.66 16.66 52.50 15.55 60.35 68.05 112.85
## C 8.00 6.23 0.63 53.41 5.41 60.00 5.05 49.85 65.05 109.85
## D 9.00 5.83 0.23 50.00 2.00 67.50 1.87 46.67 69.37 114.17
## E 10.00 5.69 0.09 48.76 0.76 75.00 0.71 45.51 75.71 120.51
## F 11.00 5.63 0.03 48.28 0.28 82.50 0.26 45.06 82.76 127.56
## G 12.00 5.61 0.01 48.10 0.10 90.00 0.10 44.90 90.10 134.90</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Evaluating the status quo inspection station</span>
lam_f <-<span class="st"> </span><span class="dv">7</span> <span class="co">#the prints arrive randomly at an inspection station at the same mean rate as the sheets arrived at the presses (7 per hour)</span>
mu_f <-<span class="st"> </span><span class="dv">8</span> <span class="co">#Each inspection takes him 7.5 minutes, so he can inspect 8 prints per hour</span>
p <-<span class="st"> </span><span class="dv">8</span> <span class="co">#The cost of in-process inventory is estimated to be $8 per hour for each poster sheet at the presses or each print at the inspection station</span>
cost <-<span class="st"> </span><span class="dv">17</span> <span class="co"># the current inspector is in a lower job classification where the compensation is $17 per hour</span>
s <-<span class="st"> </span><span class="kw">ceiling</span>(lam_f<span class="op">/</span>mu_f) <span class="co"># lam < s*mu in steady state, so rearrange to find min s </span>
n <-<span class="st"> </span><span class="dv">3</span> <span class="co"># Max number of servers</span>
p4_i <-<span class="st"> </span><span class="kw">vector</span>() <span class="co"># Create matrix to hold data</span>
<span class="co"># Loop to find low cost</span>
<span class="cf">for</span> (i <span class="cf">in</span> s<span class="op">:</span>n){
q1_fi <-<span class="st"> </span><span class="kw">NewInput.MMC</span>(lam_f, mu_f, i) <span class="co"># Set and check the inputs of the model</span>
q1_f <-<span class="st"> </span><span class="kw">QueueingModel.i_MMC</span>(q1_fi) <span class="co"># Solve the queueing model</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq is the portion of L that is idle (queue waiting)</span>
L <-<span class="st"> </span>q1_f<span class="op">$</span>L <span class="co"># Mean number of print in queue system </span>
Lq <-<span class="st"> </span>q1_f<span class="op">$</span>Lq <span class="co"># Mean number of print in queue</span>
W <-<span class="st"> </span>q1_f<span class="op">$</span>W<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue system</span>
Wq <-<span class="st"> </span>q1_f<span class="op">$</span>Wq<span class="op">*</span><span class="dv">60</span> <span class="co"># Mean minutes print wait time in queue waiting for service</span>
CL <-<span class="st"> </span>Lq<span class="op">*</span>p <span class="co"># Cost of in-process inventory in queue</span>
CL2 <-<span class="st"> </span>L<span class="op">*</span>p <span class="co"># Cost of in-process inventory </span>
CS <-<span class="st"> </span>i<span class="op">*</span>cost <span class="co"># Cost of servers in queue system</span>
p4_i <-<span class="st"> </span><span class="kw">rbind</span>(p4_i, <span class="kw">round</span>(<span class="kw">c</span>(i, L, Lq, W, Wq, CS, CL, CL2, CL <span class="op">+</span><span class="st"> </span>CS, CL2 <span class="op">+</span><span class="st"> </span>CS), <span class="dv">2</span>)) <span class="co"># Organize in table</span>
}
<span class="kw">colnames</span>(p4_i) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Servers"</span>, <span class="st">"L"</span>, <span class="st">"Lq"</span>, <span class="st">"W"</span>, <span class="st">"Wq"</span>, <span class="st">"CS"</span>, <span class="st">"CLq"</span>, <span class="st">"CL"</span>, <span class="st">"TCq"</span>, <span class="st">"TC"</span>)
<span class="kw">as.table</span>(p4_i)</code></pre></div>
<pre><code>## Servers L Lq W Wq CS CLq CL TCq TC
## A 1.00 7.00 6.12 60.00 52.50 17.00 49.00 56.00 66.00 73.00
## B 2.00 1.08 0.21 9.28 1.78 34.00 1.66 8.66 35.66 42.66
## C 3.00 0.90 0.03 7.73 0.23 51.00 0.21 7.21 51.21 58.21</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 9 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">1</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 9 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 203.15 254.95
## [2,] 172.81 224.61</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rbind</span>(p4_i[<span class="dv">1</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>], <span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">2</span>, <span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]<span class="op">+</span>p4_p[<span class="dv">2</span>,<span class="dv">9</span><span class="op">:</span><span class="dv">10</span>]) <span class="co">#using 10 presses and 2 inspector</span></code></pre></div>
<pre><code>## TCq TC
## [1,] 134.05 185.85
## [2,] 103.71 155.51</code></pre>
</div>
<div id="proposed-increasing-print-time" class="section level3">
<h3>Proposed: Increasing print time</h3>
<p>By contrast, if we were increasing the print time, it would increase the cost of the power for running each press by $7.50 per hour but the total cost would <strong>decrease by $9.63 per hour</strong>, from current total cost of $203.14 per hour to $193.51.38 per hour (highlighted green in table 3). This decrease is due to the decrease in cost of in-process inventory, by increasing the print time the average WIP inventory goes from 7-8 posters to about 5-6 posters in printing press. Although the cost of printing press increases by $5, the result shows that the cost of in-process inventory WIP decreases from $60.14 per hours to $45.51 per hour (highlighted red in table 3). This proposed solution should be considered in our final decision.</p>
<p><em>Table 3: Increasing print time by 15 mins would decrease the Total Cost by $9.63 per hour </em></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p4b <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(p4_p[<span class="dv">5</span>,],<span class="co">#using 10 presses and 1 inspector</span>
p4_i[<span class="dv">1</span>,])) <span class="co">#using 10 presses and 1 inspector</span>
<span class="co">#L is the all inventory (work in process) of the queue system while Lq (ork To Process)is the portion of L that is idle (queue waiting)</span>
<span class="kw">rownames</span>(p4b) <-<span class="st"> </span><span class="kw">c</span>( <span class="st">"Servers"</span>,
<span class="st">"Average number of posters (WIP)"</span>,
<span class="st">"Average number of posters in queue (WIQ)"</span>,
<span class="st">"Average wait time (WIP) (in Minutes)"</span>,
<span class="st">"Average wait time in queue waiting for service (WIQ) (in Minutes)"</span>,
<span class="st">"Cost of servers"</span>,
<span class="st">"Cost of in-process inventory waiting for service (WIQ) ($ Per Hour)"</span>,
<span class="st">"Cost of all in-process inventory (WIP) ($ Per Hour)"</span>,
<span class="st">"Cost of in-process inventory in queue including server cost (WIQ) ($ Per Hour)"</span>,
<span class="st">"Total Cost ($ Per Hour)"</span>)
<span class="kw">colnames</span>(p4b)<-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Printing Press"</span>,<span class="st">"Inspection Station"</span>)
p4b<span class="op">$</span>Total =<span class="st"> </span><span class="kw">rowSums</span>(p4b)
now2 <-<span class="st"> </span><span class="kw">cbind</span>(now, p4b)
<span class="kw">options</span>(<span class="dt">knitr.kable.NA =</span> <span class="st">''</span>)
now2 <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable</span>(<span class="st">"html"</span>, <span class="dt">row.names =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">kable_styling</span>(<span class="dt">bootstrap_options =</span> <span class="st">"striped"</span>, <span class="dt">full_width =</span> F, <span class="dt">position =</span> <span class="st">"left"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now2), <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"green"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">row_spec</span>(<span class="kw">nrow</span>(now2)<span class="op">-</span><span class="dv">2</span>, <span class="dt">bold =</span> T, <span class="dt">color =</span> <span class="st">"white"</span>, <span class="dt">background =</span> <span class="st">"darkred"</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">4</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">column_spec</span>(<span class="dv">7</span>, <span class="dt">bold =</span> T) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">add_header_above</span>(<span class="kw">c</span>(<span class="st">" "</span>, <span class="st">"Current"</span> =<span class="st"> </span><span class="dv">3</span>, <span class="st">"Proposed: Increasing print time"</span> =<span class="st"> </span><span class="dv">3</span>))</code></pre></div>
<table class="table table-striped" style="width: auto !important; ">
<thead>
<tr>
<th style="border-bottom:hidden" colspan="1">
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Current
</div>
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px;">
Proposed: Increasing print time
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
<th style="text-align:right;">
Printing Press
</th>
<th style="text-align:right;">
Inspection Station
</th>
<th style="text-align:right;">
Total
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Servers
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
<td style="text-align:right;">
10.00
</td>
<td style="text-align:right;">
1.00
</td>
<td style="text-align:right;font-weight: bold;">
11.00
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters (WIP)
</td>
<td style="text-align:right;">
7.52
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
14.52
</td>
<td style="text-align:right;">
5.69
</td>
<td style="text-align:right;">
7.00
</td>
<td style="text-align:right;font-weight: bold;">
12.69
</td>
</tr>
<tr>
<td style="text-align:left;">
Average number of posters in queue (WIQ)
</td>
<td style="text-align:right;">
0.52
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.64
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
6.12
</td>
<td style="text-align:right;font-weight: bold;">
6.21
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time (WIP) (in Minutes)
</td>
<td style="text-align:right;">
64.43
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
124.43
</td>
<td style="text-align:right;">
48.76
</td>
<td style="text-align:right;">
60.00
</td>
<td style="text-align:right;font-weight: bold;">
108.76
</td>
</tr>
<tr>
<td style="text-align:left;">
Average wait time in queue waiting for service (WIQ) (in Minutes)
</td>
<td style="text-align:right;">
4.43
</td>
<td style="text-align:right;">
52.50
</td>
<td style="text-align:right;font-weight: bold;">