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.

List of Figures xiii 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