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Dynamic lot-size model

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denote the minimal cost program for periods 1 to t. If at period t* the minimum in F(t) occurs for j = t** ≤ t*, then in periods t > t* it is sufficient to consider only t** ≤ j ≤ t. In particular, if t* = t**, then it is sufficient to consider programs such that
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EA Silver, HC Meal, A heuristic for selecting lot size quantities for the case of a deterministic time-varying demand rate and discrete opportunities for replenishment, Production and inventory management,
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Federgruen, Awi, and Michal Tzur. "A simple forward algorithm to solve general dynamic lot sizing models with n periods in 0 (n log n) or 0 (n) time."
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Jans, Raf, and Zeger Degraeve. "Meta-heuristics for dynamic lot sizing: a review and comparison of solution approaches."
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to the costs of acting optimally for periods 1 to t**-1 determined in the previous iteration of the algorithm
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model that takes into account that demand for the product varies over time. The model was introduced by
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Economic lot sizing: an O (n log n) algorithm that runs in linear time in the Wagner-Whitin case
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Given that I = 0 for period t, it is optimal to consider periods 1 through t - 1 by themselves
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Consider the policies of ordering at period t**, t** = 1, 2, ... , t*, and filling demands
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From these t* alternatives, select the minimum cost policy for periods 1 through t*
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The precedent theorems are used in the proof of the Planning Horizon Theorem. Let
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to order now to minimize the sum of setup cost and inventory cost. Let us denote
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over a relevant time horizon t=1,2,...,N (for example we might know how many
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can also vary with time if desired). The problem is how many units
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and T. Whitin, "Dynamic version of the economic lot size model,"
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Solving the Lot Sizing Problem using the Wagner-Whitin Algorithm
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The functional equation representing minimal cost policy is:
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will be needed each week for the next 52 weeks). There is a
857:{\displaystyle F(t)=\min \left \atop s_{t}+F(t-1)}\right]} 488:. Wagner and Whitin proved the following four theorems: 537: 646: 573:{\displaystyle x_{t}=\textstyle \sum _{j=t}^{k}d_{j}} 523: 506:
There exists an optimal program such that ∀t: either
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A dynamic lot-sizing model with demand time windows
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Start with t*=1: 892:dynamic programming 1146:Management Science 1134:Management Science 1115:Management Science 1106:Management Science 854: 694: 605:, t**<t*, then 580:for some k (t≤k≤N) 570: 569: 472: 351: 252: 1058:Wagelmans, Albert 1046:Thomson M. Whitin 848: 673: 484:Where H() is the 350: 296: 48:Thomson M. Whitin 16:(Redirected from 1193: 1083: 1079: 1073: 1055: 1049: 1042:Harvey M. Wagner 1039: 1016:Base stock model 1000:Newsvendor model 954: 944: 933: 922: 908: 877: 863: 861: 860: 855: 853: 849: 847: 825: 824: 814: 813: 809: 787: 786: 777: 776: 766: 761: 739: 728: 710: 709: 695: 693: 626: 615: 604: 593: 579: 577: 576: 571: 568: 567: 557: 552: 533: 532: 516: 502: 481: 479: 478: 473: 471: 467: 466: 462: 461: 460: 448: 447: 427: 426: 408: 407: 395: 394: 373: 372: 352: 349: 348: 347: 335: 334: 318: 311: 310: 282: 281: 261: 259: 258: 253: 245: 244: 234: 223: 205: 204: 194: 183: 165: 164: 136: 125: 114: 103: 89: 71: 44:Harvey M. Wagner 36:inventory theory 21: 1201: 1200: 1196: 1195: 1194: 1192: 1191: 1190: 1176: 1175: 1156: 1092: 1090:Further reading 1087: 1086: 1080: 1076: 1062:Stan Van Hoesel 1056: 1052: 1040: 1029: 1024: 983: 953: 952: 948: 945: 943: 942: 938: 935: 932: 931: 927: 924: 921: 920: 916: 913: 907: 906: 902: 899: 884: 876: 875: 871: 868: 816: 815: 778: 768: 701: 700: 696: 677: 671: 665: 642: 641: 636: 625: 624: 620: 617: 614: 613: 609: 606: 603: 602: 598: 595: 592: 591: 587: 584: 559: 524: 519: 518: 515: 514: 510: 507: 501: 500: 496: 493: 452: 439: 432: 428: 412: 399: 386: 358: 357: 353: 339: 326: 319: 302: 301: 273: 268: 267: 236: 196: 156: 145: 144: 135: 134: 130: 127: 124: 123: 119: 116: 113: 112: 108: 105: 102: 101: 97: 94: 88: 87: 83: 80: 70: 69: 65: 62: 56: 28: 23: 22: 15: 12: 11: 5: 1199: 1197: 1189: 1188: 1178: 1177: 1174: 1173: 1167: 1162: 1155: 1154:External links 1152: 1151: 1150: 1138: 1126: 1119: 1110: 1091: 1088: 1085: 1084: 1074: 1050: 1026: 1025: 1023: 1020: 1019: 1018: 1013: 1008: 1002: 996: 990: 982: 979: 963: 962: 959: 956: 950: 949: 946: 940: 939: 936: 929: 928: 925: 918: 917: 914: 910: 904: 903: 900: 883: 880: 873: 872: 869: 852: 846: 843: 840: 837: 834: 831: 828: 823: 819: 812: 808: 805: 802: 799: 796: 793: 790: 785: 781: 775: 771: 765: 760: 757: 754: 751: 748: 744: 738: 735: 732: 727: 724: 721: 717: 713: 708: 704: 699: 692: 689: 686: 683: 680: 676: 668: 664: 661: 658: 655: 652: 649: 635: 632: 631: 630: 627: 622: 621: 618: 611: 610: 607: 600: 599: 596: 589: 588: 585: 581: 566: 562: 556: 551: 548: 545: 541: 536: 531: 527: 512: 511: 508: 504: 498: 497: 494: 470: 465: 459: 455: 451: 446: 442: 438: 435: 431: 425: 422: 419: 415: 411: 406: 402: 398: 393: 389: 385: 382: 379: 376: 371: 368: 365: 361: 356: 346: 342: 338: 333: 329: 325: 322: 317: 314: 309: 305: 299: 294: 291: 288: 285: 280: 276: 251: 248: 243: 239: 233: 230: 227: 222: 219: 216: 212: 208: 203: 199: 193: 190: 187: 182: 179: 176: 172: 168: 163: 159: 155: 152: 132: 131: 128: 121: 120: 117: 110: 109: 106: 99: 98: 95: 85: 84: 81: 67: 66: 63: 55: 52: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 1198: 1187: 1184: 1183: 1181: 1171: 1168: 1166: 1163: 1161: 1158: 1157: 1153: 1148: 1147: 1142: 1139: 1136: 1135: 1130: 1127: 1124: 1120: 1117: 1116: 1111: 1108: 1107: 1102: 1098: 1094: 1093: 1089: 1078: 1075: 1071: 1067: 1063: 1059: 1054: 1051: 1047: 1043: 1038: 1036: 1034: 1032: 1028: 1021: 1017: 1014: 1012: 1011:Reorder point 1009: 1007: 1003: 1001: 997: 995: 991: 989: 985: 984: 980: 978: 976: 972: 968: 960: 957: 911: 897: 896: 895: 893: 889: 882:The algorithm 881: 879: 864: 850: 841: 838: 835: 829: 826: 821: 817: 810: 803: 800: 797: 791: 788: 783: 779: 773: 769: 763: 758: 755: 752: 749: 746: 742: 736: 733: 730: 725: 722: 719: 715: 711: 706: 702: 697: 690: 687: 684: 681: 678: 666: 659: 653: 647: 639: 633: 628: 582: 564: 560: 554: 549: 546: 543: 539: 534: 529: 525: 505: 491: 490: 489: 487: 482: 468: 463: 457: 453: 449: 444: 440: 436: 433: 429: 423: 420: 417: 413: 409: 404: 400: 391: 387: 380: 377: 374: 369: 366: 363: 359: 354: 344: 340: 336: 331: 327: 323: 320: 315: 312: 307: 303: 292: 286: 278: 274: 265: 262: 249: 246: 241: 237: 231: 228: 225: 220: 217: 214: 210: 206: 201: 197: 191: 188: 185: 180: 177: 174: 170: 166: 161: 157: 153: 150: 142: 140: 93: 79: 75: 61: 54:Problem setup 53: 51: 49: 45: 41: 37: 33: 19: 1144: 1132: 1122: 1113: 1104: 1077: 1066:Antoon Kolen 1053: 964: 885: 865: 640: 637: 483: 266: 263: 143: 92:holding cost 57: 31: 29: 1141:H.M. Wagner 1129:H.M. Wagner 973:(e.g., the 967:too complex 1022:References 971:heuristics 78:setup cost 888:algorithm 839:− 801:− 743:∑ 734:− 716:∑ 682:≤ 540:∑ 450:− 367:− 337:≥ 313:≥ 247:≥ 229:− 211:∑ 207:− 189:− 171:∑ 139:inventory 50:in 1958. 1180:Category 981:See also 878:> 0. 74:widgets 1064:, and 912:Add H( 517:=0 or 503:=0; ∀t 1082:1973 1044:and 688:< 115:and 46:and 30:The 1103:." 1099:. " 1068:. " 951:t** 941:t** 930:t** 919:t** 675:min 663:min 623:t** 601:t** 298:min 34:in 1182:: 1060:, 1030:^ 874:t* 590:t* 141:: 947:I 937:i 934:+ 926:s 923:) 915:x 905:t 901:d 870:x 851:] 845:) 842:1 836:t 833:( 830:F 827:+ 822:t 818:s 811:] 807:) 804:1 798:j 795:( 792:F 789:+ 784:k 780:d 774:h 770:i 764:t 759:1 756:+ 753:h 750:= 747:k 737:1 731:t 726:j 723:= 720:h 712:+ 707:j 703:s 698:[ 691:t 685:j 679:1 667:[ 660:= 657:) 654:t 651:( 648:F 619:x 612:t 608:d 597:x 586:d 565:j 561:d 555:k 550:t 547:= 544:j 535:= 530:t 526:x 513:t 509:x 499:t 495:x 469:] 464:) 458:t 454:d 445:t 441:x 437:+ 434:I 430:( 424:1 421:+ 418:t 414:f 410:+ 405:t 401:s 397:) 392:t 388:x 384:( 381:H 378:+ 375:I 370:1 364:t 360:i 355:[ 345:t 341:d 332:t 328:x 324:+ 321:I 316:0 308:t 304:x 293:= 290:) 287:I 284:( 279:t 275:f 250:0 242:j 238:d 232:1 226:t 221:1 218:= 215:j 202:j 198:x 192:1 186:t 181:1 178:= 175:j 167:+ 162:0 158:I 154:= 151:I 133:t 129:x 122:t 118:i 111:t 107:s 100:t 96:i 86:t 82:s 68:t 64:d 20:)

Index

Dynamic lot size model
inventory theory
economic order quantity
Harvey M. Wagner
Thomson M. Whitin
forecast of product demand
widgets
setup cost
holding cost
inventory
Heaviside step function
algorithm
dynamic programming
too complex
heuristics
Silver-Meal heuristic
Economic order quantity
Economic production quantity
Newsvendor model
Economic lot scheduling problem
Reorder point
Base stock model




Harvey M. Wagner
Thomson M. Whitin
Wagelmans, Albert
Stan Van Hoesel

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