Knowledge (XXG)

Open-shop scheduling

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208:, the order in which the processing steps happen can vary freely. The goal is to assign a time for each job to be processed by each workstation, so that no two jobs are assigned to the same workstation at the same time, no job is assigned to two workstations at the same time, and every job is assigned to each workstation for the desired amount of time. The usual measure of quality of a solution is its 232:
that has the jobs and workstations as its vertices, and that has an edge for every job-workstation pair that has a nonzero processing time. The color of an edge in the coloring corresponds to the segment of time at which a job-workstation pair is scheduled to be processed. Because the
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workstations, and a two-dimensional table of the amount of time each job should spend at each workstation (possibly zero). Each job may be processed only at one workstation at a time, and each workstation can process only one job at a time. However, unlike the
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for instances that have only two workstations or only two jobs. It may also be solved in polynomial time when all nonzero processing times are equal: in this case the problem becomes equivalent to
212:, the amount of time from the start of the schedule (the first assignment of a job to a workstation) to its end (the finishing time of the last job at the last workstation). 185: 157: 426: 419: 266:
is a similar problem but with a yet additional constraint – the operations must be done in a specific order.
76:- the total length of the schedule (that is, when all the jobs have finished processing). In the specific variant known as 465: 455: 187:" is a 3-machines job-shop problem with unit processing times, where the goal is to minimize the maximum completion time. 412: 574: 460: 450: 481: 248:
For three or more workstations, or three or more jobs, with varying processing times, open-shop scheduling is
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constraint – each operation must be done on a specific machine.
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machines with varying processing power, while trying to minimize the
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The input to the open-shop scheduling problem consists of a set of
408: 245:, bipartite graphs may be edge-colored in polynomial time. 68:
of varying processing times, which need to be scheduled on
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in the first field. For example, the problem denoted by "
300:(1976), "Open shop scheduling to minimize finish time", 121:
three-field notation for optimal job-scheduling problems
166: 135: 358:; Hall, L. A.; Hoogeveen, J. A.; Hurkens, C. A. J.; 43:. In a general job-scheduling problem, we are given 541: 505: 474: 443: 220:The open-shop scheduling problem can be solved in 179: 151: 274:is a job-shop scheduling but with an additional 172: 420: 8: 427: 413: 405: 315: 171: 165: 140: 134: 108:order. The problem was first studied by 288: 123:, the open-shop variant is denoted by 7: 14: 104:which need to be processed in an 366:(1997), "Short shop schedules", 80:, each job consists of a set of 1: 21:open-shop scheduling problem 591: 180:{\displaystyle C_{\max }} 216:Computational complexity 554:Truthful job scheduling 506:Optimization objectives 362:; Sevast'janov, S. V.; 436:Optimal job scheduling 181: 153: 152:{\displaystyle p_{ij}} 41:optimal job scheduling 380:10.1287/opre.45.2.288 326:10.1145/321978.321985 199:jobs, another set of 182: 154: 39:. It is a variant of 271:Flow-shop scheduling 164: 133: 78:open-shop scheduling 29:optimization problem 17:Open-shop scheduling 549:Interval scheduling 368:Operations Research 296:Gonzalez, Teofilo; 263:Job-shop scheduling 110:Teofilo F. Gonzalez 37:operations research 575:Optimal scheduling 542:Other requirements 466:Unrelated machines 456:Identical machines 303:Journal of the ACM 177: 149: 562: 561: 356:Williamson, D. P. 97:, ...,  61:, ...,  582: 475:Multi-stage jobs 461:Uniform machines 429: 422: 415: 406: 399: 398: 352: 346: 344: 319: 293: 256:Related problems 239:bipartite graphs 206:job-shop problem 186: 184: 183: 178: 176: 175: 158: 156: 155: 150: 148: 147: 119:In the standard 33:computer science 590: 589: 585: 584: 583: 581: 580: 579: 565: 564: 563: 558: 537: 501: 470: 439: 433: 403: 402: 354: 353: 349: 317:10.1.1.394.1507 295: 294: 290: 285: 258: 230:bipartite graph 222:polynomial time 218: 193: 167: 162: 161: 136: 131: 130: 102: 96: 89: 66: 60: 53: 12: 11: 5: 588: 586: 578: 577: 567: 566: 560: 559: 557: 556: 551: 545: 543: 539: 538: 536: 535: 530: 525: 520: 515: 509: 507: 503: 502: 500: 499: 494: 489: 484: 482:Parallel tasks 478: 476: 472: 471: 469: 468: 463: 458: 453: 451:Single machine 447: 445: 444:One-stage jobs 441: 440: 434: 432: 431: 424: 417: 409: 401: 400: 374:(2): 288–294, 360:Lenstra, J. K. 347: 310:(4): 665–679, 287: 286: 284: 281: 280: 279: 267: 257: 254: 243:perfect graphs 217: 214: 192: 189: 174: 170: 146: 143: 139: 100: 94: 87: 64: 58: 51: 13: 10: 9: 6: 4: 3: 2: 587: 576: 573: 572: 570: 555: 552: 550: 547: 546: 544: 540: 534: 531: 529: 526: 524: 521: 519: 516: 514: 511: 510: 508: 504: 498: 495: 493: 490: 488: 485: 483: 480: 479: 477: 473: 467: 464: 462: 459: 457: 454: 452: 449: 448: 446: 442: 437: 430: 425: 423: 418: 416: 411: 410: 407: 397: 393: 389: 385: 381: 377: 373: 369: 365: 364:Shmoys, D. B. 361: 357: 351: 348: 343: 339: 335: 331: 327: 323: 318: 313: 309: 305: 304: 299: 298:Sahni, Sartaj 292: 289: 282: 277: 273: 272: 268: 265: 264: 260: 259: 255: 253: 251: 246: 244: 240: 236: 231: 227: 226:edge coloring 223: 215: 213: 211: 207: 202: 198: 190: 188: 168: 160: 144: 141: 137: 126: 122: 117: 115: 111: 107: 103: 93: 86: 83: 79: 75: 71: 67: 57: 50: 46: 42: 38: 34: 30: 26: 22: 18: 486: 371: 367: 350: 307: 301: 291: 275: 269: 261: 247: 219: 200: 196: 194: 128: 124: 118: 114:Sartaj Sahni 105: 98: 91: 84: 81: 77: 69: 62: 55: 48: 44: 24: 20: 16: 15: 235:line graphs 533:Throughput 283:References 191:Definition 82:operations 528:Tardiness 518:Earliness 492:Flow shop 487:Open shop 312:CiteSeerX 116:in 1976. 106:arbitrary 569:Category 523:Lateness 513:Makespan 497:Job shop 438:problems 210:makespan 74:makespan 27:) is an 396:1644998 342:1642775 334:0429089 250:NP-hard 90:,  54:,  394:  388:171745 386:  340:  332:  314:  384:JSTOR 338:S2CID 47:jobs 276:flow 241:are 112:and 35:and 25:OSSP 376:doi 322:doi 237:of 173:max 129:O3| 31:in 19:or 571:: 392:MR 390:, 382:, 372:45 370:, 336:, 330:MR 328:, 320:, 308:23 306:, 252:. 228:a 428:e 421:t 414:v 378:: 345:. 324:: 201:m 197:n 169:C 159:| 145:j 142:i 138:p 125:O 101:n 99:O 95:2 92:O 88:1 85:O 70:m 65:n 63:J 59:2 56:J 52:1 49:J 45:n 23:(

Index

optimization problem
computer science
operations research
optimal job scheduling
makespan
Teofilo F. Gonzalez
Sartaj Sahni
three-field notation for optimal job-scheduling problems
job-shop problem
makespan
polynomial time
edge coloring
bipartite graph
line graphs
bipartite graphs
perfect graphs
NP-hard
Job-shop scheduling
Flow-shop scheduling
Sahni, Sartaj
Journal of the ACM
CiteSeerX
10.1.1.394.1507
doi
10.1145/321978.321985
MR
0429089
S2CID
1642775
Williamson, D. P.

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