Rights Contact Login For More Details
More About This Title Scheduling in Supply Chains Using Mixed Integer Programming
English
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.
English
English
List of Tables xix
Preface xxiii
Acknowledgments xxv
Introduction xxvii
Part One Short-Term Scheduling in Supply Chains 1
1. Scheduling of Flexible Flow Shops 3
1.1 Introduction 3
1.2 Mixed Integer Programs for Scheduling Flow Shops 4
1.3 Constructive Heuristics for Scheduling Flexible Flow Shops 19
1.4 Scheduling Flow Shops with Limited Machine Availability 30
1.5 Computational Examples 32
1.6 Comments 37
Exercises 40
2. Scheduling of Surface Mount Technology Lines 41
2.1 Introduction 41
2.2 SMT Line Configurations 42
2.3 General Scheduling of SMT Lines 45
2.4 Batch Scheduling of SMT Lines 51
2.5 An Improvement Heuristic for Scheduling SMT Lines 54
2.6 Computational Examples 60
2.7 Comments 69
Exercises 70
3. Balancing and Scheduling of Flexible Assembly Lines 71
3.1 Introduction 71
3.2 Balancing and Scheduling of Flexible Assembly Lines with Infinite In-Process Buffers 72
3.3 Balancing and Scheduling of SMT Lines 83
3.4 Comments 97
Exercises 97
4. Loading and Scheduling of Flexible Assembly Systems 99
4.1 Introduction 99
4.2 Loading and Scheduling of Flexible Assembly Systems with Single Stations and Infinite In-Process Buffers 100
4.3 Loading and Scheduling of Flexible Assembly Systems with Parallel Stations and Finite In-Process Buffers 110
4.4 Comments 125
Exercises 130
Part Two Medium-Term Scheduling in Supply Chains 131
5. Customer Order Acceptance and Due Date Setting in Make-to-Order Manufacturing 133
5.1 Introduction 133
5.2 Problem Description 134
5.3 Bi-Objective Order Acceptance and Due Date Setting 137
5.4 Lexicographic Approach 141
5.5 Scheduling of Customer Orders 144
5.6 Computational Examples 148
5.7 Comments 158
Exercises 159
6. Aggregate Production Scheduling in Make-to-Order Manufacturing 161
6.1 Introduction 161
6.2 Problem Description 163
6.3 Bi-Objective Scheduling of Customer Orders 165
6.4 Multi-Objective Scheduling of Customer Orders 171
6.5 Scheduling of Single-Period Customer Orders 187
6.6 Comments 212
Exercises 216
7. Reactive Aggregate Production Scheduling in Make-to-Order Manufacturing 219
7.1 Introduction 219
7.2 Problem Description 220
7.3 Mixed Integer Programs for Reactive Scheduling 221
7.4 Rescheduling Algorithms 224
7.5 Input and Output Inventory 227
7.6 Computational Examples 229
7.7 Comments 236
Exercises 236
8. Scheduling of Material Supplies in Make-to-Order Manufacturing 239
8.1 Introduction 239
8.2 Flexible vs. Cyclic Material Supplies 241
8.3 Model Enhancements 244
8.4 Computational Examples 248
8.5 Comments 256
Exercises 257
9. Selection of Static Supply Portfolio in Supply Chains with Risks 259
9.1 Introduction 259
9.2 Selection of a Supply Portfolio without Discount under Operational Risks 261
9.3 Selection of Supply Portfolio with Discount under Operational Risks 266
9.4 Computational Examples 269
9.5 Selection of Supply Portfolio under Disruption Risks 272
9.6 Single-Objective Supply Portfolio under Disruption Risks 274
9.7 Bi-Objective Supply Portfolio under Disruption Risks 279
9.8 Computational Examples 280
9.9 Comments 289
Exercises 291
10. Selection of a Dynamic Supply Portfolio in Supply Chains with Risks 293
10.1 Introduction 293
10.2 Multiperiod Supplier Selection and Order Allocation 294
10.3 Selection of a Dynamic Supply Portfolio to Minimize Expected Costs 297
10.4 Selection of a Dynamic Supply Portfolio to Minimize Expected Worst-Case Costs 301
10.5 Supply Portfolio for Best-Case and Worst-Case TDN Supplies 302
10.6 Computational Examples 306
10.7 Comments 315
Exercises 316
Part Three Coordinated Scheduling in Supply Chains 319
11. Hierarchical Integration of Medium- and Short-Term Scheduling 321
11.1 Introduction 321
11.2 Problem Description 322
11.3 Medium-Term Production Scheduling 325
11.4 Short-Term Machine Assignment and Scheduling 330
11.5 Computational Examples 335
11.6 Comments 348
Exercises 349
12. Coordinated Scheduling in Supply Chains with a Single Supplier 351
12.1 Introduction 351
12.2 Problem Description 352
12.3 Supply Chain Inventory 354
12.4 Coordinated Supply Chain Scheduling: An Integrated Approach 359
12.5 Coordinated Supply Chain Scheduling: A Hierarchical Approach 362
12.6 Computational Examples 366
12.7 Comments 375
Exercises 376
13. Coordinated Scheduling in Supply Chains with Assignment of Orders to Suppliers 379
13.1 Introduction 379
13.2 Problem Description 380
13.3 Conditions for Feasibility of Customer Due Dates 383
13.4 Coordinated Supply Chain Scheduling: An Integrated Approach 385
13.5 Selected Multi-Objective Solution Approaches 392
13.6 Coordinated Supply Chain Scheduling: A Hierarchical Approach 393
13.7 Computational Examples 401
13.8 Comments 408
Exercises 409
14. Coordinated Scheduling in Supply Chains without Assignment of Orders to Suppliers 411
14.1 Introduction 411
14.2 Problem Description 412
14.3 Coordinated Supply Chain Scheduling: An Integrated Approach 413
14.4 Selected Bi-Objective Solution Approaches 418
14.5 Coordinated Supply Chain Scheduling: A Hierarchical Approach 419
14.6 Computational Examples 425
14.7 Comments 433
Exercises 434
References 437
Index 449
English
“Scheduling in supply chains using mixed integer programming is a comprehensive resource for practitioners and researchers working in supply chain planning, scheduling, and management.” (Mathematical Reviews, 2012)
"The book is well written and is addressed to practitioners and researchers working in supply chain management and scheduling and as well for PhD students in management science, applied mathematics, operations research, industrial engineering and computer science." (Zentralblatt MATH, 2012)"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." (Finwin, 8 November 2011)
