Principles of Sequencing and Scheduling

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Format: Hardcover
Pub. Date: 2009-04-13
Publisher(s): Wiley
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Summary

Sequencing and scheduling rely heavily on decision making and can have a major impact on the productivity of a process. Written by two well-known experts in the field, Principles of Sequencing and Scheduling thoroughly discusses the fundamental and core topics of scheduling theory. An expanded, updated, and more comprehensive version of Kenneth Baker's two previous works, this accessible text thoroughly covers deterministic models and includes recent developments and approaches of stochastic models. This is an ideal reference for practicing applied mathematicians, statisticians, computer scientists, operations researchers, and industrial engineers.

Author Biography

Kenneth R. Baker, PhD, is Nathaniel Leverone Professor of Management at Dartmouth College. A Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), Dr. Baker has published extensively in his areas of research interest, which include mathematical modeling, spreadsheet engineering, and scheduling. He is the coauthor of Management Science: The Art of Modeling with Spreadsheets, Second Edition, also published by Wiley. Dan Trietsch, PhD, is Professor of Industrial Engineering at the American University of Armenia. He has authored over thirty journal articles on topics such as network design, statistical quality control, and various aspects of scheduling.

Table of Contents

Prefacep. xiii
Introductionp. 1
Introduction to Sequencing and Schedulingp. 1
Scheduling Theoryp. 3
Philosophy and Coverage of the Bookp. 6
Referencesp. 8
Single-Machine Sequencingp. 10
Introductionp. 10
Preliminariesp. 11
Problems Without Due Dates: Elementary Resultsp. 15
Flowtime and Inventoryp. 15
Minimizing Total Flowtimep. 16
Minimizing Total Weighted Flowtimep. 19
Problems with Due Dates: Elementary Resultsp. 21
Lateness Criteriap. 21
Minimizing the Number of Tardy Jobsp. 24
Minimizing Total Tardinessp. 25
Due Dates as Decisionsp. 29
Summaryp. 31
Referencesp. 31
Exercisesp. 32
Optimization Methods for the Single-Machine Problemp. 34
Introductionp. 34
Adjacent Pairwise Interchange Methodsp. 36
A Dynamic Programming Approachp. 37
Dominance Propertiesp. 43
A Branch and Bound Approachp. 47
Summaryp. 53
Referencesp. 55
Exercisesp. 55
Heuristic Methods for the Single-Machine Problemp. 57
Introductionp. 57
Dispatching and Construction Proceduresp. 58
Random Samplingp. 63
Neighborhood Search Techniquesp. 66
Tabu Searchp. 70
Simulated Annealingp. 72
Genetic Algorithmsp. 74
The Evolutionary Solverp. 75
Summaryp. 79
Referencesp. 81
Exercisesp. 81
Earliness and Tardiness Costsp. 86
Introductionp. 86
Minimizing Deviations from a Common Due Datep. 88
Four Basic Resultsp. 88
Due Dates as Decisionsp. 93
The Restricted Versionp. 94
Asymmetric Earliness and Tardiness Costsp. 96
Quadratic Costsp. 99
Job-Dependent Costsp. 100
Distinct Due Datesp. 101
Summaryp. 104
Referencesp. 105
Exercisesp. 105
Sequencing for Stochastic Schedulingp. 108
Introductionp. 108
Basic Stochastic Counterpart Modelsp. 109
The Deterministic Counterpartp. 115
Minimizing the Maximum Costp. 117
The Jensen Gapp. 122
Stochastic Dominance and Associationp. 123
Using Risk Solverp. 127
Summaryp. 132
Referencesp. 134
Exercisesp. 134
Safe Schedulingp. 137
Introductionp. 137
Meeting Service-Level Targetsp. 138
Trading Off Tightness and Tardinessp. 141
The Stochastic E/T Problemp. 145
Setting Release Datesp. 149
The Stochastic U-Problem: A Service-Level Approachp. 152
The Stochastic U-Problem: An Economic Approachp. 156
Summaryp. 160
Referencesp. 161
Exercisesp. 162
Extensions of the Basic Modelp. 165
Introductionp. 165
Nonsimultaneous Arrivalsp. 166
Minimizing the Makespanp. 169
Minimizing Maximum Tardinessp. 171
Other Measures of Performancep. 172
Related Jobsp. 174
Minimizing Maximum Tardinessp. 175
Minimizing Total Flowtime with Stringsp. 176
Minimizing Total Flowtime with Parallel Chainsp. 178
Sequence-Dependent Setup Timesp. 181
Dynamic Programming Solutionsp. 183
Branch and Bound Solutionsp. 184
Heuristic Solutionsp. 189
Stochastic Models with Sequence-Dependent Setup Timesp. 190
Setting Tight Due Datesp. 191
Revisiting the Tightness/Tardiness Trade-offp. 192
Summaryp. 195
Referencesp. 196
Exercisesp. 197
Parallel-Machine Modelsp. 200
Introductionp. 200
Minimizing the Makespanp. 201
Nonpreemptable Jobsp. 202
Nonpreemptable Related Jobsp. 208
Preemptable Jobsp. 211
Minimizing Total Flowtimep. 213
Stochastic Modelsp. 217
The Makespan Problem with Exponential Processing Timesp. 218
Safe Scheduling with Parallel Machinesp. 220
Summaryp. 221
Referencesp. 222
Exercisesp. 223
Flow Shop Schedulingp. 225
Introductionp. 225
Permutation Schedulesp. 228
The Two-Machine Problemp. 230
Johnson's Rulep. 230
A Proof of Johnson's Rulep. 232
The Model with Time Lagsp. 234
The Model with Setupsp. 235
Special Cases of The Three-Machine Problemp. 236
Minimizing the Makespanp. 237
Branch and Bound Solutionsp. 238
Heuristic Solutionsp. 241
Variations of the m-Machine Modelp. 243
Ordered Flow Shopsp. 243
Flow Shops with Blockingp. 244
No-Wait Flow Shopsp. 245
Summaryp. 247
Referencesp. 248
Exercisesp. 249
Stochastic Flow Shop Schedulingp. 251
Introductionp. 251
Stochastic Counterpart Modelsp. 252
Safe Scheduling Models with Stochastic Independencep. 258
Flow Shops with Linear Associationp. 261
Empirical Observationsp. 262
Summaryp. 267
Referencesp. 268
Exercisesp. 269
Lot Streaming Procedures for the Flow Shopp. 271
Introductionp. 271
The Basic Two-Machine Modelp. 273
Preliminariesp. 273
The Continuous Versionp. 274
The Discrete Versionp. 277
Models with Setupsp. 279
The Three-Machine Model with Consistent Sublotsp. 281
The Continuous Versionp. 281
The Discrete Versionp. 284
The Three-Machine Model with Variable Sublotsp. 285
Item and Batch Availabilityp. 285
The Continuous Versionp. 285
The Discrete Versionp. 287
Computational Experimentsp. 290
The Fundamental Partitionp. 292
Defining the Fundamental Partitionp. 292
A Heuristic Procedure for s Sublotsp. 295
Summaryp. 295
Referencesp. 297
Exercisesp. 298
Scheduling Groups of Jobsp. 300
Introductionp. 300
Scheduling Job Familiesp. 301
Minimizing Total Weighted Flowtimep. 302
Minimizing Maximum Latenessp. 304
Minimizing Makespan in the Two-Machine Flow Shopp. 306
Scheduling with Batch Availabilityp. 309
Scheduling with a Batch Processorp. 313
Minimizing the Makespan with Dynamic Arrivalsp. 314
Minimizing Makespan in the Two-Machine Flow Shopp. 315
Minimizing Total Flowtime with Dynamic Arrivalsp. 317
Batch-Dependent Processing Timesp. 318
Summaryp. 320
Referencesp. 321
Exercisesp. 322
The Job Shop Problemp. 325
Introductionp. 325
Types of Schedulesp. 328
Schedule Generationp. 333
The Shifting Bottleneck Procedurep. 337
Bottleneck Machinesp. 338
Heuristic and Optimal Solutionsp. 339
Neighborhood Search Heuristicsp. 342
Summaryp. 345
Referencesp. 346
Exercisesp. 347
Simulation Models for the Dynamic Job Shopp. 349
Introductionp. 349
Model Elementsp. 350
Types of Dispatching Rulesp. 352
Reducing Mean Flowtimep. 354
Meeting Due Datesp. 357
Backgroundp. 357
Some Clarifying Experimentsp. 362
Experimental Resultsp. 364
Summaryp. 369
Referencesp. 370
Network Methods for Project Schedulingp. 372
Introductionp. 372
Logical Constraints and Network Constructionp. 373
Temporal Analysis of Networksp. 376
The Time/Cost Trade-offp. 381
Traditional Probabilistic Network Analysisp. 385
The PERT Methodp. 385
Theoretical Limitations of PERTp. 389
Summaryp. 393
Referencesp. 394
Exercisesp. 395
Resource-Constrained Project Schedulingp. 398
Introductionp. 398
Extending the Job Shop Modelp. 399
Extending the Project Modelp. 405
Heuristic Construction and Search Algorithmsp. 407
Construction Heuristicsp. 408
Neighborhood Search Improvement Schemesp. 410
Selecting Priority Listsp. 412
Summaryp. 414
Referencesp. 415
Exercisesp. 415
Safe Scheduling for Projectsp. 418
Introductionp. 418
Stochastic Balance Principles For Activity Networksp. 420
The Assembly Coordination Modelp. 420
Balancing a General Project Networkp. 426
Additional Examplesp. 428
Hierarchical Balancingp. 434
Crashing Stochastic Activitiesp. 436
Summaryp. 439
Referencesp. 441
Exercisesp. 441
Practical Processing Time Distributionsp. 445
Important Processing Time Distributionsp. 445
The Uniform Distributionp. 445
The Exponential Distributionp. 446
The Normal Distributionp. 447
The Lognormal Distributionp. 447
The Parkinson Distributionp. 449
Increasing and Decreasing Completion Ratesp. 450
Stochastic Dominancep. 451
Linearly Associated Processing Timesp. 452
Referencesp. 458
The Critical Ratio Rulep. 459
A Basic Trade-off Problemp. 459
Optimal Policy for Discrete Probability Modelsp. 461
A Special Discrete Case: Equally Likely Outcomesp. 463
Optimal Policy for Continuous Probability Modelsp. 463
A Special Continuous Case: The Normal Distributionp. 467
Calculating d + yE(T) for the Normal Distributionp. 469
Referencesp. 470
Integer Programming Models for Sequencingp. 471
Introductionp. 471
The Single-Machine Modelp. 472
Sequence-Position Decisionsp. 472
Precedence Decisionsp. 473
Time-Indexed Decisionsp. 473
The Flow Shop Modelp. 475
Referencesp. 477
Name Indexp. 479
Subject Indexp. 483
Table of Contents provided by Ingram. All Rights Reserved.

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