Scheduling in Supply Chains Using Mixed Integer Programming

A unified, systematic approach to applying mixed integer programming solutions to integrated scheduling in customer-driven supply chains Supply chain management is a rapidly developing field, and the recent improvements in modeling, preprocessing, solution algorithms, and mixed integer programming (MIP) software have made it possible to solve large-scale MIP models of scheduling problems, especially integrated scheduling in supply chains. Featuring a unified and systematic presentation, Scheduling in Supply Chains Using Mixed Integer Programming provides state-of-the-art MIP modeling and solutions approaches, equipping readers with the knowledge and tools to model and solve real-world supply chain scheduling problems in make-to-order manufacturing. Drawing upon the author's own research, the book explores MIP approaches and examples-which are modeled on actual supply chain scheduling problems in high-tech industries-in three comprehensive sections: Short-Term Scheduling in Supply Chains presents various MIP models and provides heuristic algorithms for scheduling flexible flow shops and surface mount technology lines, balancing and scheduling of Flexible Assembly Lines, and loading and scheduling of Flexible Assembly Systems Medium-Term Scheduling in Supply Chains outlines MIP models and MIP-based heuristic algorithms for supplier selection and order allocation, customer order acceptance and due date setting, material supply scheduling, and medium-term scheduling and rescheduling of customer orders in a make-to-order discrete manufacturing environment Coordinated Scheduling in Supply Chains explores coordinated scheduling of manufacturing and supply of parts as well as the assembly of products in supply chains with a single producer and single or multiple suppliers; MIP models for a single- or multiple-objective decision making are also provided Two main decision-making approaches are discussed and compared throughout. The integrated (simultaneous) approach, in which all required decisions are made simultaneously using complex, monolithic MIP models; and the hierarchical (sequential) approach, in which the required decisions are made successively using hierarchies of simpler and smaller-sized MIP models. Throughout the book, the author provides insight on the presented modeling tools using AMPL® modeling language and CPLEX solver. Scheduling in Supply Chains Using Mixed Integer Programming is a comprehensive resource for practitioners and researchers working in supply chain planning, scheduling, and management. The book is also appropriate for graduate- and PhD-level courses on supply chains for students majoring in management science, industrial engineering, operations research, applied mathematics, and computer science. Contents......Page 8 List of Figures......Page 14 List of Tables......Page 20 Preface......Page 24 Acknowledgments......Page 26 Introduction......Page 28 Part One Short-Term Scheduling in Supply Chains......Page 37 1.1 INTRODUCTION......Page 39 1.2.1 Scheduling Flow Shops with Infinit In-Process Buffers......Page 40 1.2.2 Scheduling Flow Shops with Finite In-Process Buffers......Page 44 1.2.3 Alternative Objective Functions......Page 47 1.2.5 Reentrant Flow Shops......Page 48 1.2.6 Scheduling Modes......Page 49 1.2.7 Computational Examples......Page 52 1.3.1 A Fast Heuristic for Scheduling Flow Shops with Finite In-Process Buffers......Page 55 1.3.2 A Fast Heuristic for Scheduling Flow Shops with No In-Process Buffers......Page 60 1.3.3 Computational Examples......Page 61 1.4 SCHEDULING FLOW SHOPS WITH LIMITED MACHINE AVAILABILITY......Page 66 1.5 COMPUTATIONAL EXAMPLES......Page 68 1.5.1 Scheduling with Continuous Machine Availability......Page 69 1.5.2 Scheduling with Limited Machine Availability......Page 70 1.6 COMMENTS......Page 73 EXERCISES......Page 76 2.1 INTRODUCTION......Page 77 2.2 SMT LINE CONFIGURATIONS......Page 78 2.2.1 SMT Lines for Single-Sided Boards......Page 79 2.2.2 SMT Lines for Double-Sided Boards......Page 80 2.3 GENERAL SCHEDULING OF SMT LINES......Page 81 2.3.1 Special Cases......Page 86 2.4 BATCH SCHEDULING OF SMT LINES......Page 87 2.5 AN IMPROVEMENT HEURISTIC FOR SCHEDULING SMT LINES......Page 90 2.5.1 Sequencing......Page 91 2.5.2 Assignment and Timing......Page 92 2.5.3 Special Cases......Page 93 2.6 COMPUTATIONAL EXAMPLES......Page 96 2.7 COMMENTS......Page 105 EXERCISES......Page 106 3.1 INTRODUCTION......Page 107 3.2 BALANCING AND SCHEDULING OF FLEXIBLE ASSEMBLY LINES WITH INFINITE IN-PROCESS BUFFERS......Page 108 3.2.1 Simultaneous Balancing and Scheduling......Page 109 3.2.2 Sequential Balancing and Scheduling......Page 113 3.2.3 Computational Examples......Page 116 3.3 BALANCING AND SCHEDULING OF SMT LINES......Page 119 3.3.1 Simultaneous Balancing and Scheduling of an SMT Line......Page 121 3.3.2 Sequential Balancing and Scheduling of an SMT Line......Page 127 3.3.3 Computational Examples......Page 128 EXERCISES......Page 133 4.1 INTRODUCTION......Page 135 4.2 LOADING AND SCHEDULING OF FLEXIBLE ASSEMBLY SYSTEMS WITH SINGLE STATIONS AND INFINITE IN-PROCESS BUFFERS......Page 136 4.2.1 Simultaneous Loading and Scheduling......Page 137 4.2.2 Sequential Loading and Scheduling......Page 141 4.2.3 Computational Examples......Page 144 4.3 LOADING AND SCHEDULING OF FLEXIBLE ASSEMBLY SYSTEMS WITH PARALLEL STATIONS AND FINITE IN-PROCESS BUFFERS......Page 146 4.3.1 Simultaneous Loading and Scheduling......Page 147 4.3.2 Sequential Loading and Scheduling......Page 154 4.3.3 Computational Examples......Page 158 4.4 COMMENTS......Page 161 EXERCISES......Page 166 Part Two Medium-Term Scheduling in Supply Chains......Page 167 5.1 INTRODUCTION......Page 169 5.2 PROBLEM DESCRIPTION......Page 170 5.2.1 Critical Load Index......Page 172 5.3 BI-OBJECTIVE ORDER ACCEPTANCE AND DUE DATE SETTING......Page 173 5.4 LEXICOGRAPHIC APPROACH......Page 177 5.4.1 Model Enhancements......Page 179 5.5 SCHEDULING OF CUSTOMER ORDERS......Page 180 5.6 COMPUTATIONAL EXAMPLES......Page 184 5.7 COMMENTS......Page 194 EXERCISES......Page 195 6.1 INTRODUCTION......Page 197 6.2 PROBLEM DESCRIPTION......Page 199 6.3 BI-OBJECTIVE SCHEDULING OF CUSTOMER ORDERS......Page 201 6.4 MULTI-OBJECTIVE SCHEDULING OF CUSTOMER ORDERS......Page 207 6.4.1 Maximum Level of Total Inventory vs. Maximum Earliness of Customer Orders......Page 210 6.4.2 Finite Capacity of Input, Output, and Central Buffers......Page 211 6.4.3 Computational examples......Page 212 6.5 SCHEDULING OF SINGLE-PERIOD CUSTOMER ORDERS......Page 223 6.5.1 Cutting Constraints......Page 228 6.5.2 Computational Examples......Page 231 6.6 COMMENTS......Page 248 EXERCISES......Page 252 7.1 INTRODUCTION......Page 255 7.2 PROBLEM DESCRIPTION......Page 256 7.3 MIXED INTEGER PROGRAMS FOR REACTIVE SCHEDULING......Page 257 7.3.1 Basic Model......Page 258 7.4 RESCHEDULING ALGORITHMS......Page 260 7.5 INPUT AND OUTPUT INVENTORY......Page 263 7.6 COMPUTATIONAL EXAMPLES......Page 265 EXERCISES......Page 272 8.1 INTRODUCTION......Page 275 8.2 FLEXIBLE vs. CYCLIC MATERIAL SUPPLIES......Page 277 8.3 MODEL ENHANCEMENTS......Page 280 8.3.1 Finite Input Buffer......Page 281 8.3.2 Safety Lead Time......Page 283 8.4 COMPUTATIONAL EXAMPLES......Page 284 8.5 COMMENTS......Page 292 EXERCISES......Page 293 9.1 INTRODUCTION......Page 295 9.2 SELECTION OF A SUPPLY PORTFOLIO WITHOUT DISCOUNT UNDER OPERATIONAL RISKS......Page 297 9.3 SELECTION OF SUPPLY PORTFOLIO WITH DISCOUNT UNDER OPERATIONAL RISKS......Page 302 9.4 COMPUTATIONAL EXAMPLES......Page 305 9.5 SELECTION OF SUPPLY PORTFOLIO UNDER DISRUPTION RISKS......Page 308 9.6.1 Value-at-Risk vs. Conditional Value-at-Risk......Page 310 9.6.2 Minimization of Expected Costs......Page 312 9.6.3 Minimization of Expected Worst-Case Costs......Page 313 9.7 BI-OBJECTIVE SUPPLY PORTFOLIO UNDER DISRUPTION RISKS......Page 315 9.8 COMPUTATIONAL EXAMPLES......Page 316 9.9 COMMENTS......Page 325 EXERCISES......Page 327 10.1 INTRODUCTION......Page 329 10.2 MULTIPERIOD SUPPLIER SELECTION AND ORDER ALLOCATION......Page 330 10.3 SELECTION OF A DYNAMIC SUPPLY PORTFOLIO TO MINIMIZE EXPECTED COSTS......Page 333 10.4 SELECTION OF A DYNAMIC SUPPLY PORTFOLIO TO MINIMIZE EXPECTED WORST-CASE COSTS......Page 337 10.5 SUPPLY PORTFOLIO FOR BEST-CASE AND WORST-CASE TDN SUPPLIES......Page 338 10.5.1 Minimization of Expected Costs......Page 339 10.5.2 Minimization of Expected Worst-Case Costs......Page 340 10.6 COMPUTATIONAL EXAMPLES......Page 342 10.6.1 Scenarios with at Most One or with Consecutive Disruptions......Page 345 10.7 COMMENTS......Page 351 EXERCISES......Page 352 Part Three Coordinated Scheduling in Supply Chains......Page 354 11.1 INTRODUCTION......Page 357 11.2 PROBLEM DESCRIPTION......Page 358 11.3 MEDIUM-TERM PRODUCTION SCHEDULING......Page 361 11.3.1 Cutting Constraints for Multicapacity Machines......Page 363 11.4 SHORT-TERM MACHINE ASSIGNMENT AND SCHEDULING......Page 366 11.4.1 Machine Assignment......Page 367 11.4.2 Machine Scheduling......Page 369 11.5 COMPUTATIONAL EXAMPLES......Page 371 11.5.1 Material Availability......Page 379 11.6 COMMENTS......Page 384 EXERCISES......Page 385 12.1 INTRODUCTION......Page 387 12.2 PROBLEM DESCRIPTION......Page 388 12.3 SUPPLY CHAIN INVENTORY......Page 390 12.4 COORDINATED SUPPLY CHAIN SCHEDULING: AN INTEGRATED APPROACH......Page 395 12.5 COORDINATED SUPPLY CHAIN SCHEDULING: A HIERARCHICAL APPROACH......Page 398 12.6 COMPUTATIONAL EXAMPLES......Page 402 12.7 COMMENTS......Page 411 EXERCISES......Page 412 13.1 INTRODUCTION......Page 415 13.2 PROBLEM DESCRIPTION......Page 416 13.3 CONDITIONS FOR FEASIBILITY OF CUSTOMER DUE DATES......Page 419 13.4.1 Decision Variables......Page 421 13.4.2 Objective Functions: Customer Service Level vs. Total Cost......Page 423 13.4.3 A Multi-Objective Monolithic Model......Page 424 13.4.4 Multiperiod Orders......Page 427 13.5.2 Reference Point-Based Scalarizing Program......Page 428 13.6 COORDINATED SUPPLY CHAIN SCHEDULING: A HIERARCHICAL APPROACH......Page 429 13.6.1 Rough-Cut Capacity Allocation......Page 431 13.6.2 Scheduling of Customer Orders......Page 434 13.6.3 Scheduling Manufacturing and Delivery of Parts......Page 435 13.7 COMPUTATIONAL EXAMPLES......Page 437 13.8 COMMENTS......Page 444 EXERCISES......Page 445 14.1 INTRODUCTION......Page 447 14.2 PROBLEM DESCRIPTION......Page 448 14.3.1 Decision Variables......Page 449 14.3.2 Objective Functions: Lost Revenue vs. Inventory Holding Cost......Page 450 14.3.3 A Bi-Objective Monolithic Model......Page 452 14.4.1 Weighted-Sum Program......Page 454 14.5 COORDINATED SUPPLY CHAIN SCHEDULING: A HIERARCHICAL APPROACH......Page 455 14.5.1 Due Date Assignment......Page 456 14.5.2 Scheduling of Customer Orders......Page 458 14.5.3 Scheduling Manufacturing and Delivery of Parts......Page 459 14.6 COMPUTATIONAL EXAMPLES......Page 461 14.7 COMMENTS......Page 469 EXERCISES......Page 470 References......Page 473 Index......Page 485 Supply chain management is a rapidly developing field within management science and operations research, and this book presents a unified, systematic, and practical approach to applying mixed integer programming (MIP) modeling and solutions to integrated scheduling in customer driven supply chains.  Two main decision-making approaches are discussed, compared, and illustrated with computational examples (modeled on real-world supply chains) throughout the book: the integrated (simultaneous) approach, in which all required decisions are made simultaneously using complex, monolithic MIP models and the hierarchical (sequential) approach, in which the required decisions are made successively, using hierarchies of simpler and smaller size MIP models.  The book is divided into three parts: Part I (Chapters 1 to 4) considers short term scheduling, presents various MIP models, and provides some heuristic algorithms for detailed scheduling in flexible flowshops and general flexible assembly systems; Part II (Chapters 5 to 10) focuses on medium term decision-making in supply chains and presents MIP models and some MIP-based heuristic algorithms for supplier selection, customer order acceptance, due date setting, as well as the corresponding aggregate scheduling and rescheduling of orders in make-to-order discrete manufacturing environment; and Part III (Chapters 11 to 14) focuses on coordinated scheduling of the manufacturing and supply of parts and assembly of finished products and provides MIP models for a single or multiple objective decision making. Featuring a unified and systematic presentation, this book provides state-of-the-art mixed integer programming (MIP) modeling and solution approaches, equipping readers with the knowledge and tools to model and solve real-world supply chain scheduling problems in make-to-order manufacturing.