The Palgrave handbook of operations research:
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[2022]
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| adam_text | Contents Part I 1 2 Discrete (Combinatorial) Optimisation Bilevel Discrete Optimisation: Computational Complexity and Applications Yury Kochetov, Alexander Plyasunov, and Arteam Panin 1.1 Introduction 1.2 Main Definitions and Properties 1.3 Computational Methods 1.3.1 Exact Methods 1.3.2 Metaheuristics 1.4 Computational and Approximation Complexity 1.4.1 Polynomial Hierarchy 1.4.2 -Hard Bilevel Programming Problems 1.4.3 Approximation Hierarchy 1.5 Applications 1.6 Conclusion References Discrete Location Problems with Uncertainty Nader Azizi, Sergio Garcia, and Chandra Ade Irawan 2.1 Introduction 2.2 The Single-Source CapacitatedFacility Location Problem with Uncertainty 2.3 Covering Location Problems with Uncertainty 3 3 5 10 10 12 16 16 17 20 22 30 31 43 43 44 49 xiii
xiv Contents Set Covering Models with Uncertainty Maximum Covering Models with Uncertainty 2.3.3 Other Covering Location Models 2.4 Hub Location Problems with Uncertainty 2.4.1 Demand, Cost, and Time Uncertainty 2.4.2 Network Reliability and Resilience 2.4.3 Hub Interdiction and Fortification 2.5 Conclusions References 2.3.1 2.3.2 3 4 49 51 54 54 56 61 64 64 65 Integrated Vehicle Routing Problems: A Survey Gianfranco Guastaroba,. Andrea Mor, and Μ. Grazia Speranza 3.1 Introduction 3.2 Inventory Routing Problems 3.3 Location Routing Problems 3.4 Routing in Combination with Packing: Routing with Loading Constraints 3.4.1 Introduction to the Class of Problems 3.4.2 The Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints81 3.4.3 The Capacitated Vehicle Routing Problem with Three-Dimensional Loading Constraints 86 3.5 Routing in Combination with Routing: Two-Echelon Routing Problems 3.5- 1 Introduction tothe Class of Problems 3-5.2 The Two-EchelonCapacitated Vehicle Routing Problem Յ.5.Յ Recent Trends in the Literature on 2E-VRPs 3.6 Conclusions References The Knapsack Problem and Its Variants: Formulations and Solution Methods Christophe Wilbaut, Saïd Hanafi, Igor Machado Coelho, and Abilio Lucena 4.1 Introduction 4.2 Variants with a Single Knapsack Constraint 4.2.1 The Multiple-Choice Knapsack Problem 4.2.2 The Discounted Knapsack Problem 73 73 76 78 79 80 90 90 92 95 98 98 105 105 108 109 112
Contents The Knapsack Problem with Setup The Knapsack Problem with Neighbor Constraints 4.2.5 The Knapsack Constrained Maximum Spanning Tree Problem 4.2.6 The Set Union Knapsack Problem 4.2.7 The Precedence Constrained Knapsack Problem 4.2.8 The Disjunctively Constrained Knapsack Problem 4.2.9 The Product Knapsack Problem 4.3 Variants with Multiple Knapsack Constraints 4.3.1 The Multidimensional Knapsack Problem 4.3.2 The Multidimensional Knapsack Problem with Demand Constraints 4.3.3 The Multiple Knapsack Problem 4.3.4 The Compartmentalized Knapsack Problem 4.3.5 The Multiple Knapsack Problem with Color Constraints 4.4 Conclusion and Suggestions References 113 Rank Aggregation: Models and Algorithms Javier Alcaraz, Mercedes Landete, andJuan F. Monge 5.1 Introduction 5.2 Ranking of Elements 5.2.1 Linear Ordering Problem 5.2.2 Rank Aggregation Problem in Cyclic Sequences 159 5.2.3 Target Visitation Problem 5.2.4 The Center Ranking Problem 5.3 Ranking of Sets from a Ranking of Elements 5.3.1 The Optimal Bucket Order Problem 5.3.2 The Linear Ordering Problem with Clusters 5.3.3 The Linear Ordering Problem of Sets 5.3.4 The Center Ranking Problem of Sets 5.4 Feasible Regions Similarities and Differences Through a Small Example References 153 4.2.3 4.2.4 5 XV 115 116 117 119 120 121 122 122 126 128 132 134 137 138 153 155 156 161 163 164 165 166 168 170 171 176
xvi Contents Part II Continuous (Global) Optimisation 6 7 Multi-Objective Optimization: Methods and Applications Dylan F. Jones and Helenice O. Florentino 6.1 Introduction to Multi-Objective Optimization 6.2 Solving a Multi-objective Problem 181 6.2.1 Basic Concepts 6.2.2 Pareto Set Generation 6.3 Goal Programming 6.4 Compromise Programming 6.5 Applications and Usage 6.5.1 Application of Techniques 6.5.2 Use of Techniques 6.6 Conclusions References 182 183 183 185 192 195 200 200 202 203 203 Competitive Facilities Location 209 Tammy Drezner 7.1 Introduction 7.2 Approaches to Estimating Market Share 7.2.1 Deterministic Rules 7.2.2 Probabilistic Rules 7.2.3 Estimating Attractiveness 7.3 Distance Correction 7.4 Extensions 7.4.1 Minimax Regret Criterion . 7.4.2 The Threshold Objective 7.4.3 Leader-Follower Models 7.4.4 Location and Design 7.4.5 Lost Demand 7.4.6 Cannibalization 7.5 Solution Methods 7.5.1 Single Facility 7.5.2 Multiple Facilities 7.5.3 The TLA Method 7.6 Applying the Gravity Rule to Other Objectives 7.6.1 Gravity y -Median 7.6.2 Gravity Hub Location 7.6.3 Gravity Multiple Server 7.7 Summary and Suggestions for Future Research References 209 211 211 211 213 214 215 215 216 217 220 222 223 226 226 226 226 227 227 227 228 228 229
Contents xvii 8 Interval Tools in Branch-and-Bound Methods for Global Optimization 237 José Fernández and Boglárka G.-Tóth 8.1 Introduction 237 8.1.1 Interval Analysis 238 8.1.2 Aim of the Chapter 241 8.2 Prototype Interval B B Method 242 8.2.1 Bounding Rule 243 8.2.2 Elimination/Filtering Rules 243 8.2.3 Selection Rule 245 8.2.4 Division Rules 246 8.2.5 Termination Rule 246 8.2.6 Interval B B Methods forMINLP Problems 247 8.3 Bounding Techniques 247 8.4 Discarding Tests 252 8.5 Selection of the Next Box to BeProcessed 254 8.6 Subdivision Rule 255 8.7 Software 256 8.7.1 Libraries 257 8.7.2 Packages with Interval B B Implementations 258 8.8 Interval B B Methods forMINLP Problems 259 References 261 9 Continuous Facility Location Problems Zvi Drezner 9.1 Introduction 9.2 Single Facility Location Problems 9.2.1 The Weber (One-Median) Location Problem 9.2.2 The Minimax (One-Center) Location Problem 9.2.3 The Obnoxious Facility Location Problem 9.2.4 Equity Models 9.2.5 Location on a Sphere 9-3 Multiple Facilities Location Problems 9.3.1 Conditional Models 9.3.2 The /»-Median Location Problem 9.3.3 The /»-Center Location Problem 9.3.4 Cover Models 269 269 270 270 274 274 275 276 278 278 279 280 280
xviii Contents The Multiple Obnoxious Facilities Location Problem 9.3.6 Equity Models 9.Յ.7 Not Necessarily Patronizing the Closest Facility 9.4 Solution Methods 9.4.1 Generating Replicable Test Problems 9.4.2 Solving Single Facility Problems 9.4.3 Multiple Facilities Solution Methods 9.5 Summary and Suggestions for Future Research References 9.3.5 10 Data Envelopment Analysis: Recent Developments and. Challenges AU Emrouznejad, Guo-liang Yang, Mohammad Khoveyni, and Maria Michali 10.1 Introduction 10.2 Background 10.2.1 The CCR Model in the Envelopment and Multiplier Forms 10.2.2 The BCC Model in Envelopment and Multiplier Forms 311 10.2.3 The Additive Model 10.3 A Short Literature Survey and Trend of Publications on DEA 10.3.1 Statistics Based on Publication Years 10.3.2 Statistics Based on Journals 10.3.3 Statistics Based on Authors 10.3.4 Statistics Based on Keywords 10.3.5 Statistics Based on Page Numbers (Size) 10.4 Recent Theoretical Developments in DEA 10.4.1 Network DEA 10.4.2 Stochastic DEA 10.4.3 Fuzzy DEA 10.4.4 Bootstrapping 10.4.5 Directional Measure and Negative Data 10.4.6 Undesirable Factors 10.4.7 Directional Returns to Scale in DEA IO.5 Recent Applications and Future Developments 10.6 Conclusions References 282 284 284 285 285 286 292 294 295 307 307 309 309 312 313 313 313 314 315 315 317 317 324 328 331 336 342 345 346 347 347
Contents Part III 11 xix Heuristic Search Optimisation An Overview of Heuristics and Metaheuristics Saïd Salhi andJonathan Thompson 11.1 Introduction 11.1.1 Optimisation Problems 11.1.2 The Need for Heuristics 11.1.3 Some Characteristics of Heuristics 11.1.4 Performance of Heuristics 11.1.5 Heuristic Classification and Categorisation 11.2 Improvement-Only Heuristics 11.2.1 Hill-Climbing Methods 11.2.2 Classical Multi-Start 11.2.3 Greedy Randomised Adaptive Search Procedure (GRASP) 11.2.4 Variable Neighbourhood Search (VNS) 11.2.5 Iterated Local Search (ILS) 11.2.6 A Multi-Level Composite Heuristic 11.2.7 Problem Perturbation Heuristics 11.2.8 Some Other Improving Only Methods 11.3 Not Necessarily Improving Heuristics 11.3.1 Simulated Annealing 11.3.2 Threshold-Accepting Heuristics 11.3.3 Tabu Search 11.4 Population-Based Heuristics 11.4.1 Genetic Algorithms 11.4.2 Ant Colony Optimisation 11.4.3 The Bee Algorithm 11.4.4 Particle Swarm Optimisation (PSO) 11.4.5 A Brief Summary of Other Population-Based Approaches 11.5 Some Applications 11.5.1 Radio-Therapy 11.5.2 Sport Management 11.5.3 Educational Timetabling 11.5.4 Nurse Rostering 11.5.5 Distribution Management (Routing) 11.5.6 Location Problems 11.5.7 Chemical Engineering 11.5.8 Civil Engineering Applications 11.6 Conclusion and Research Issues 353 353 355 357 357 358 359 359 360 361 363 364 366 366 367 369 370 370 373 374 377 377 381 384 386 387 389 390 390 391 391 391 392 393 393 394
XX Contents Conclusion Potential Research Issues 394 395 396 Formulation Space Search Metaheuristic Nenad Mladenovič, Jack Brimberg, and Dragan Uroševič 12.1 Introduction 12.2 Literature Review 12.3 Methodology 12.3.1 Stochastic FSS 12.3.2 Deterministic FSS 12.3.3 Variable Neighborhood FSS and Variants 12.4 Some Applications 12.4.1 Circle Packing Problem 12.4.2 Graph Coloring Problem 12.4.3 Continuous Location Problems 12.5 Conclusions References 405 11.6.1 11.6.2 References 405 409 412 412 413 415 420 420 427 431 441 442 Sine Cosine Algorithm: Introduction and Advances Anjali Rawat, Shitu Singh, andJagdish Chand Bansal 13.1 Introduction 13.2 Sine Cosine Algorithm (SCA) 13.3 Parameters Involved in the SCA 13.4 Advances in the Sine Cosine Algorithm 13.4.1 Extension of SCA 13.5 Conclusion References 447 Less Is More Approach in Heuristic Optimization Nenad Mladenovič, Zvi Drezner, Jack Brimberg, and Dragan Uroševič 14.1 Introduction 14.2 Literature Review of LIMA Implementations 14.3 LIMA Algorithm 14.3.1 What Is More and What Is Less in Algorithm Design 14.3.2 Steps of the LIMA Algorithm 14.4 Applications of LIMA 14.4.1 Minimum Differential Dispersion Problem 14.4.2 Planar /»-Median Problem 469 447 448 450 450 452 463 464 469 471 474 474 476 476 477 479
Contents xxi 14.4.3 Multiple Obnoxious Facility Location Problem 14.4.4 Gray Patterns 14.4.5 Discussion 14.5 Conclusions References 15 487 490 492 493 The New Era of Hybridisation and Learning in Heuristic Search Design Saïd Salhi andJonathan Thompson 15.1 Hybridisation Search 15.1.1 Hybridisation of Heuristics with Heuristics 15.1.2 Integrating Heuristics within Exact Methods 15.1.3 Integration of ILP within Heuristics/Metaheuristics 15.2 Big Data and Machine Learning 15.2.1 Decision Trees and Random Forest (RM) 15.2.2 Support Vector Machines (SVM) 15.2.3 Neural Networks (NN) I5.2.4 MLs Evaluation Measures І5.З Deep Learning Heuristics 15.4 Implementation Issues in Heuristic Search Design I5.4.I Data Structure 15.4.2 Duplication Identification І5.4.З Cost Function Approximation I5.4.4 Reduction Tests/Neighbourhood Reduction I5.4.5 Parameters and Hyper Parameters Optimisation I5.4.6 Impact of Parallelisation 15.5 Conclusion and Research Issues References Part IV 16 483 501 501 502 505 511 514 516 518 519 519 521 523 523 525 526 527 529 531 532 535 Forecasting, Simulation and Prediction Forecasting with Judgment Paul Goodwin and Robert Fildes 16.1 Introduction 16.2 Why Is the Use of Judgment so Widespread in Forecasting? 16.2.1 The Benefits of Judgment in Forecasting 541 541 543 544
xxii Contents The Drawbacks of UsingJudgment in Forecasting 16.3 Strategies for Improving the Role of Judgment in Forecasting 16.3.1 Providing Feedback 16.3.2 Providing Advice 16.3.3 Restrictiveness 16.3.4 Decomposition 16.3.5 Correcting Forecasts 16.3.6 Other Improvement Strategies 16.3.7 Improving Forecasts from Groups 16.3.8 Integrating Judgment with Algorithm-based Forecasts 16.4 Conclusions References 16.2.2 17 Input Uncertainty in Stochastic Simulation Russell R. Barton, Henry Lam, and Eunhye Song 17.1 Introduction 17.2 Characterizing Input Uncertainty 17.3 Confidence Interval Construction and Variance Estimation 17.3.1 Parametric Methods 17.3.2 Nonparametric Methods 17.3.3 Empirical Likelihood 17.4 Other Aspects 17.4.1 Bias Estimation 17.4.2 Online Data 17.4.3 Data Collection vs Simulation Expense 17.4.4 Model Calibration and Inverse Problems 17.5 Simulation Optimization under Input Uncertainty 17.5.1 Selection of the Best under Input Uncertainty 17.5.2 Global Optimization under Input Uncertainty 17.5.3 Online Optimization with StreamingInput Data 17.6 FutureResearch Directions References 547 553 553 554 555 555 557 558 559 560 562 563 573 573 575 576 578 584 596 597 598 598 598 599 599 602 608 609 610 611
Contents 18 19 xxiii Fuzzy multi-attribute decision-making: Theory, methods and Applications 621 Zeshui Xu and Shen Zhang 18.1 Introduction 18.2 Literature Review of Fuzzy MADM 18.3 Fuzzy MADM Based on the Information Integration 18.3.1 Information Integration and Fuzzy MADM 18.3.2 Fuzzy Aggregation Operators 18.3.3 Supplementary Instruction 18.4 Fuzzy Measures and MADM 18.4.1 Fuzzy Measures 18.4.2 Application of Fuzzy Measures in MADM 18.5 Fuzzy Preference Relations and MADM 18.5.1 Fuzzy Preference Relations 18-5-2 Fuzzy Analytic Hierarchy Process 18.6 Some Other Classical Synthetic Fuzzy MADM Methods 641 18.6.1 Fuzzy PROMETHEE 18.6.2 Fuzzy ELECTRE 18.6.3 Hesitant Fuzzy TODIM 18.7 Prospects for Future Research Directions References Importance Measures in Reliability Engineering: An Introductory Overview Shaomin Wu and Frank Coolen 19.1 Introduction 19.2 Basic Concepts of Reliability 19.2.1 Reliability Function 19.2.2 System Reliability Analysis 19.3 Importance Measures 19.3.1 Technology-Based Measures 19.3.2 Utility-Based Importance Measures 19.3.3 Importance Measures Based on the Survival Signature 19.3.4 Importance Measures and Some Important Concepts 19.4 Information Needed for Importance Measures 19-5 Applications of Importance Measures 19.6 Concluding Remarks References 621 624 627 627 628 631 632 632 635 638 638 639 641 643 644 646 648 659 659 660 660 661 663 663 666 666 667 669 670 672 673
xxiv Contents 20 Queues with Variable Service Speeds: Exact Results and Scaling Limits 675 Moeko Yajima and Tuan Phung-Duc 20.1 Introduction 675 20.2 Queues with Vacation/setup time 676 20.2.1 Vacation Models 676 20.2.2 Models with Workload /Queue-Length-Dependent Service 678 20.2.3 Models with Setup Time 679 20.2.4 Open Problems 684 20.3 Infinite-Server Queues with Changeable Service 684 20.3.1 Stability Condition 684 20.3.2 Exact Analysis 686 20.3.3 Limit Analysis 688 References 691 21 Forecasting and its Beneficiaries Bahman Rostami-Tabar and John E. Boylan 21.1 Background 21.2 Forecasting for Social Good 21.3 Barriers to Sharing the Benefits of Forecasting 21.3.1 Access to Resources 21.3.2 Access to Expertise 21.3.3 Communication Issues 21.4 More Effective Communications 21.4.1 Forecast Producers 21.4.2 Commercial Software Developers 21.4.3 Forecasting Academics 21.5 Democratising forecasting 21.5.1 Data Science Initiatives 21.5.2 Democratising Forecasting Initiative 21.5.3 Benefits and Impact of Democratising Forecasting 21.5.4 Challenges in Delivering Democratising Forecasting Workshops 21.5.5 Limitations of the Democratising Forecasting Initiative 21.5.6 Future Agenda for Democratising Forecasting 21.6 Conclusions References 695 695 697 698 699 700 701 701 703 703 704 705 706 707 708 709 710 711 712 714
xxv Contents Part V 22 23 Problem Structuring and Behavioural OR Behavioural OR: Recent developments and future perspectives Martin Kune and Konstantinos И Katsikopoulos 22.1 Introduction 22.2 Recent Developments 22.2.1 Conceptual Framework of BOR 22.2.2 Comparison between BOR and BOM 22.2.3 Empirical Basis of BOR 22.2.4 Behavioural Science and BOR 22.3 Future Developments 22.3.1 BOR and AI 22.3.2 Theoretical and Methodological Resources for BOR 22.3.3 Education Resources for BOR 22.4 Conclusions References 721 721 722 722 723 724 725 726 726 727 730 730 731 Problem Structuring Methods: Taking Stock and Looking Ahead 735 L. Alberto Franco and Etienne A. J. A. Rouwette 23.1 Introduction 735 23.2 The Characteristics of PSMs 738 23.2.1 PSM Tools 741 23.2.2 PSM Process 743 23.3 The Use of PSMs in Practice 744 23.3.1 Sample of PSM applications 748 23.3.2 Evidence of Use and Applications 751 23.4 PSM Products, Mechanisms, Supporting Evidence 754 23.4.1 Claimed Products and Facilitative Mechanisms 754 23.4.2 Supporting Empirical Evidence 760 23.5 The Future of PSMs 763 23.5.1 Becoming a Competent PSM Practitioner 763 23.5.2 Becoming a Competent PSM Researcher 766 23.6 Conclusion 768 References 769
xxvi 24 Contents Are PSMs Relevant in a Digital Age? Towards an Ethical Dimension 781 Isabella Μ. Lami and Leroy White 24.1 Introduction 24.2 Where Are We Now? 24.2.1 What Is the Nature of the Problem Today? 24.2.2 PSMs Today 24.2.3 So What’s the Issue? 24.3 The Ethical Boundary 24.3.1 Case Vignette: Comparison of Two PSMs’ Remote Applications 790 24.4 Conclusions References ’* Part VI 25 26 781 782 783 784 785 788 795 795 Recent OR Applications Recent Advances in Big Data Analytics Daoji Li, Yinfei Kong, Zemin Zheng, andJianxin Pan 25.1 Introduction 25.2 Ultrahigh-Dimensional Data Analysis 25-2.1 Feature Screening 25.2.2 Interaction Screening for RegressionModels 25.2.3 Interaction Screening for Classification 25.3 Massive Data Analysis 25.3.1 Divide-and-Conquer Methods 25.3.2 Subsampling Methods 25.4 Summary and Discussion References OR/MS Models for the Humanitarian-Business Partnership 835 Ali Ghavamifar and S. Ali Torabi 26.1 Introduction to Humanitarian Logistics 26.2 Humanitarian Supply Chains and Their Challenges 26.3 The Incentives of Partnership During the Disaster Phases 840 26.4 Literature Review 26.4.1 Relief Procurement 26.5 Relief Warehousing, Transportation, andDistribution 26.6 Gap Analysis of Extant Literature 805 805 807 808 809 813 816 816 820 828 828 835 838 841 841 847 849
xxvii Contents 26.7 A Mathematical Model for Humanitarian-Business Partnership 26.8 Conclusion and Suggestions References 27 28 850 854 854 Drones and Delivery Robots: Models and Applications to Last Mile Delivery Cheng Chen and Emrah Demir 27.1 Introduction 27.2 Literature Review 27.2.1 Drones: Last Mile Delivery Applications 27.2.2 Delivery Robots: Last Mile Delivery Applications 27.3 Problem Description 2 7.3.1 The Estimation of Emissions 2 7.3.2 Mathematical Formulation 2 7.3.3 Solution Methodology 27 .Յ.4 Initial Solution Generation 27 .Յ.5 The Adaptive Mechanism for Deciding the Use of Operators 27 .Յ.6 The Proposed Neighborhood Operators 27.4 Numerical Analyses 27.4.1 Instances and Parameters 27.4.2 Computational Results 27.4.3 Sensitivity Analysis 27.5 Conclusions References Evaluating the Quality of Radiation Therapy Treatment Plans Using Data Envelopment Analyis Matthias Ehrgott, Andrea Raith, Glyn Shentall, John Simpson, and Emma Stubington 28.1 Introduction 28.2 The DEA Model 28.2.1 Orientation and Returns to Scale 28.3 Feature Selection 28.3.1 Expert Opinions 28.3.2 Principal Component Analysis 28.3.3 Environmental Factors 28.4 Dealing with Uncertainty 28.4.1 Simulation 859 859 861 861 863 865 866 866 870 871 871 872 874 874 875 877 878 879 883 883 885 887 887 887 888 889 890 891
xxviii Contents 28.4.2 Uncertain DEA 28.5 Application in Practice 28.5.1 Informing the Treatment Planning Process 28.5.2 Integration in the Treatment Planning Process References Index 891 892 892 894 895 899
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Contents Part I 1 2 Discrete (Combinatorial) Optimisation Bilevel Discrete Optimisation: Computational Complexity and Applications Yury Kochetov, Alexander Plyasunov, and Arteam Panin 1.1 Introduction 1.2 Main Definitions and Properties 1.3 Computational Methods 1.3.1 Exact Methods 1.3.2 Metaheuristics 1.4 Computational and Approximation Complexity 1.4.1 Polynomial Hierarchy 1.4.2 -Hard Bilevel Programming Problems 1.4.3 Approximation Hierarchy 1.5 Applications 1.6 Conclusion References Discrete Location Problems with Uncertainty Nader Azizi, Sergio Garcia, and Chandra Ade Irawan 2.1 Introduction 2.2 The Single-Source CapacitatedFacility Location Problem with Uncertainty 2.3 Covering Location Problems with Uncertainty 3 3 5 10 10 12 16 16 17 20 22 30 31 43 43 44 49 xiii
xiv Contents Set Covering Models with Uncertainty Maximum Covering Models with Uncertainty 2.3.3 Other Covering Location Models 2.4 Hub Location Problems with Uncertainty 2.4.1 Demand, Cost, and Time Uncertainty 2.4.2 Network Reliability and Resilience 2.4.3 Hub Interdiction and Fortification 2.5 Conclusions References 2.3.1 2.3.2 3 4 49 51 54 54 56 61 64 64 65 Integrated Vehicle Routing Problems: A Survey Gianfranco Guastaroba,. Andrea Mor, and Μ. Grazia Speranza 3.1 Introduction 3.2 Inventory Routing Problems 3.3 Location Routing Problems 3.4 Routing in Combination with Packing: Routing with Loading Constraints 3.4.1 Introduction to the Class of Problems 3.4.2 The Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints81 3.4.3 The Capacitated Vehicle Routing Problem with Three-Dimensional Loading Constraints 86 3.5 Routing in Combination with Routing: Two-Echelon Routing Problems 3.5- 1 Introduction tothe Class of Problems 3-5.2 The Two-EchelonCapacitated Vehicle Routing Problem Յ.5.Յ Recent Trends in the Literature on 2E-VRPs 3.6 Conclusions References The Knapsack Problem and Its Variants: Formulations and Solution Methods Christophe Wilbaut, Saïd Hanafi, Igor Machado Coelho, and Abilio Lucena 4.1 Introduction 4.2 Variants with a Single Knapsack Constraint 4.2.1 The Multiple-Choice Knapsack Problem 4.2.2 The Discounted Knapsack Problem 73 73 76 78 79 80 90 90 92 95 98 98 105 105 108 109 112
Contents The Knapsack Problem with Setup The Knapsack Problem with Neighbor Constraints 4.2.5 The Knapsack Constrained Maximum Spanning Tree Problem 4.2.6 The Set Union Knapsack Problem 4.2.7 The Precedence Constrained Knapsack Problem 4.2.8 The Disjunctively Constrained Knapsack Problem 4.2.9 The Product Knapsack Problem 4.3 Variants with Multiple Knapsack Constraints 4.3.1 The Multidimensional Knapsack Problem 4.3.2 The Multidimensional Knapsack Problem with Demand Constraints 4.3.3 The Multiple Knapsack Problem 4.3.4 The Compartmentalized Knapsack Problem 4.3.5 The Multiple Knapsack Problem with Color Constraints 4.4 Conclusion and Suggestions References 113 Rank Aggregation: Models and Algorithms Javier Alcaraz, Mercedes Landete, andJuan F. Monge 5.1 Introduction 5.2 Ranking of Elements 5.2.1 Linear Ordering Problem 5.2.2 Rank Aggregation Problem in Cyclic Sequences 159 5.2.3 Target Visitation Problem 5.2.4 The Center Ranking Problem 5.3 Ranking of Sets from a Ranking of Elements 5.3.1 The Optimal Bucket Order Problem 5.3.2 The Linear Ordering Problem with Clusters 5.3.3 The Linear Ordering Problem of Sets 5.3.4 The Center Ranking Problem of Sets 5.4 Feasible Regions Similarities and Differences Through a Small Example References 153 4.2.3 4.2.4 5 XV 115 116 117 119 120 121 122 122 126 128 132 134 137 138 153 155 156 161 163 164 165 166 168 170 171 176
xvi Contents Part II Continuous (Global) Optimisation 6 7 Multi-Objective Optimization: Methods and Applications Dylan F. Jones and Helenice O. Florentino 6.1 Introduction to Multi-Objective Optimization 6.2 Solving a Multi-objective Problem 181 6.2.1 Basic Concepts 6.2.2 Pareto Set Generation 6.3 Goal Programming 6.4 Compromise Programming 6.5 Applications and Usage 6.5.1 Application of Techniques 6.5.2 Use of Techniques 6.6 Conclusions References 182 183 183 185 192 195 200 200 202 203 203 Competitive Facilities Location 209 Tammy Drezner 7.1 Introduction 7.2 Approaches to Estimating Market Share 7.2.1 Deterministic Rules 7.2.2 Probabilistic Rules 7.2.3 Estimating Attractiveness 7.3 Distance Correction 7.4 Extensions 7.4.1 Minimax Regret Criterion . 7.4.2 The Threshold Objective 7.4.3 Leader-Follower Models 7.4.4 Location and Design 7.4.5 Lost Demand 7.4.6 Cannibalization 7.5 Solution Methods 7.5.1 Single Facility 7.5.2 Multiple Facilities 7.5.3 The TLA Method 7.6 Applying the Gravity Rule to Other Objectives 7.6.1 Gravity y -Median 7.6.2 Gravity Hub Location 7.6.3 Gravity Multiple Server 7.7 Summary and Suggestions for Future Research References 209 211 211 211 213 214 215 215 216 217 220 222 223 226 226 226 226 227 227 227 228 228 229
Contents xvii 8 Interval Tools in Branch-and-Bound Methods for Global Optimization 237 José Fernández and Boglárka G.-Tóth 8.1 Introduction 237 8.1.1 Interval Analysis 238 8.1.2 Aim of the Chapter 241 8.2 Prototype Interval B B Method 242 8.2.1 Bounding Rule 243 8.2.2 Elimination/Filtering Rules 243 8.2.3 Selection Rule 245 8.2.4 Division Rules 246 8.2.5 Termination Rule 246 8.2.6 Interval B B Methods forMINLP Problems 247 8.3 Bounding Techniques 247 8.4 Discarding Tests 252 8.5 Selection of the Next Box to BeProcessed 254 8.6 Subdivision Rule 255 8.7 Software 256 8.7.1 Libraries 257 8.7.2 Packages with Interval B B Implementations 258 8.8 Interval B B Methods forMINLP Problems 259 References 261 9 Continuous Facility Location Problems Zvi Drezner 9.1 Introduction 9.2 Single Facility Location Problems 9.2.1 The Weber (One-Median) Location Problem 9.2.2 The Minimax (One-Center) Location Problem 9.2.3 The Obnoxious Facility Location Problem 9.2.4 Equity Models 9.2.5 Location on a Sphere 9-3 Multiple Facilities Location Problems 9.3.1 Conditional Models 9.3.2 The /»-Median Location Problem 9.3.3 The /»-Center Location Problem 9.3.4 Cover Models 269 269 270 270 274 274 275 276 278 278 279 280 280
xviii Contents The Multiple Obnoxious Facilities Location Problem 9.3.6 Equity Models 9.Յ.7 Not Necessarily Patronizing the Closest Facility 9.4 Solution Methods 9.4.1 Generating Replicable Test Problems 9.4.2 Solving Single Facility Problems 9.4.3 Multiple Facilities Solution Methods 9.5 Summary and Suggestions for Future Research References 9.3.5 10 Data Envelopment Analysis: Recent Developments and. Challenges AU Emrouznejad, Guo-liang Yang, Mohammad Khoveyni, and Maria Michali 10.1 Introduction 10.2 Background 10.2.1 The CCR Model in the Envelopment and Multiplier Forms 10.2.2 The BCC Model in Envelopment and Multiplier Forms 311 10.2.3 The Additive Model 10.3 A Short Literature Survey and Trend of Publications on DEA 10.3.1 Statistics Based on Publication Years 10.3.2 Statistics Based on Journals 10.3.3 Statistics Based on Authors 10.3.4 Statistics Based on Keywords 10.3.5 Statistics Based on Page Numbers (Size) 10.4 Recent Theoretical Developments in DEA 10.4.1 Network DEA 10.4.2 Stochastic DEA 10.4.3 Fuzzy DEA 10.4.4 Bootstrapping 10.4.5 Directional Measure and Negative Data 10.4.6 Undesirable Factors 10.4.7 Directional Returns to Scale in DEA IO.5 Recent Applications and Future Developments 10.6 Conclusions References 282 284 284 285 285 286 292 294 295 307 307 309 309 312 313 313 313 314 315 315 317 317 324 328 331 336 342 345 346 347 347
Contents Part III 11 xix Heuristic Search Optimisation An Overview of Heuristics and Metaheuristics Saïd Salhi andJonathan Thompson 11.1 Introduction 11.1.1 Optimisation Problems 11.1.2 The Need for Heuristics 11.1.3 Some Characteristics of Heuristics 11.1.4 Performance of Heuristics 11.1.5 Heuristic Classification and Categorisation 11.2 Improvement-Only Heuristics 11.2.1 Hill-Climbing Methods 11.2.2 Classical Multi-Start 11.2.3 Greedy Randomised Adaptive Search Procedure (GRASP) 11.2.4 Variable Neighbourhood Search (VNS) 11.2.5 Iterated Local Search (ILS) 11.2.6 A Multi-Level Composite Heuristic 11.2.7 Problem Perturbation Heuristics 11.2.8 Some Other Improving Only Methods 11.3 Not Necessarily Improving Heuristics 11.3.1 Simulated Annealing 11.3.2 Threshold-Accepting Heuristics 11.3.3 Tabu Search 11.4 Population-Based Heuristics 11.4.1 Genetic Algorithms 11.4.2 Ant Colony Optimisation 11.4.3 The Bee Algorithm 11.4.4 Particle Swarm Optimisation (PSO) 11.4.5 A Brief Summary of Other Population-Based Approaches 11.5 Some Applications 11.5.1 Radio-Therapy 11.5.2 Sport Management 11.5.3 Educational Timetabling 11.5.4 Nurse Rostering 11.5.5 Distribution Management (Routing) 11.5.6 Location Problems 11.5.7 Chemical Engineering 11.5.8 Civil Engineering Applications 11.6 Conclusion and Research Issues 353 353 355 357 357 358 359 359 360 361 363 364 366 366 367 369 370 370 373 374 377 377 381 384 386 387 389 390 390 391 391 391 392 393 393 394
XX Contents Conclusion Potential Research Issues 394 395 396 Formulation Space Search Metaheuristic Nenad Mladenovič, Jack Brimberg, and Dragan Uroševič 12.1 Introduction 12.2 Literature Review 12.3 Methodology 12.3.1 Stochastic FSS 12.3.2 Deterministic FSS 12.3.3 Variable Neighborhood FSS and Variants 12.4 Some Applications 12.4.1 Circle Packing Problem 12.4.2 Graph Coloring Problem 12.4.3 Continuous Location Problems 12.5 Conclusions References 405 11.6.1 11.6.2 References 405 409 412 412 413 415 420 420 427 431 441 442 Sine Cosine Algorithm: Introduction and Advances Anjali Rawat, Shitu Singh, andJagdish Chand Bansal 13.1 Introduction 13.2 Sine Cosine Algorithm (SCA) 13.3 Parameters Involved in the SCA 13.4 Advances in the Sine Cosine Algorithm 13.4.1 Extension of SCA 13.5 Conclusion References 447 Less Is More Approach in Heuristic Optimization Nenad Mladenovič, Zvi Drezner, Jack Brimberg, and Dragan Uroševič 14.1 Introduction 14.2 Literature Review of LIMA Implementations 14.3 LIMA Algorithm 14.3.1 What Is More and What Is Less in Algorithm Design 14.3.2 Steps of the LIMA Algorithm 14.4 Applications of LIMA 14.4.1 Minimum Differential Dispersion Problem 14.4.2 Planar /»-Median Problem 469 447 448 450 450 452 463 464 469 471 474 474 476 476 477 479
Contents xxi 14.4.3 Multiple Obnoxious Facility Location Problem 14.4.4 Gray Patterns 14.4.5 Discussion 14.5 Conclusions References 15 487 490 492 493 The New Era of Hybridisation and Learning in Heuristic Search Design Saïd Salhi andJonathan Thompson 15.1 Hybridisation Search 15.1.1 Hybridisation of Heuristics with Heuristics 15.1.2 Integrating Heuristics within Exact Methods 15.1.3 Integration of ILP within Heuristics/Metaheuristics 15.2 Big Data and Machine Learning 15.2.1 Decision Trees and Random Forest (RM) 15.2.2 Support Vector Machines (SVM) 15.2.3 Neural Networks (NN) I5.2.4 MLs Evaluation Measures І5.З Deep Learning Heuristics 15.4 Implementation Issues in Heuristic Search Design I5.4.I Data Structure 15.4.2 Duplication Identification І5.4.З Cost Function Approximation I5.4.4 Reduction Tests/Neighbourhood Reduction I5.4.5 Parameters and Hyper Parameters Optimisation I5.4.6 Impact of Parallelisation 15.5 Conclusion and Research Issues References Part IV 16 483 501 501 502 505 511 514 516 518 519 519 521 523 523 525 526 527 529 531 532 535 Forecasting, Simulation and Prediction Forecasting with Judgment Paul Goodwin and Robert Fildes 16.1 Introduction 16.2 Why Is the Use of Judgment so Widespread in Forecasting? 16.2.1 The Benefits of Judgment in Forecasting 541 541 543 544
xxii Contents The Drawbacks of UsingJudgment in Forecasting 16.3 Strategies for Improving the Role of Judgment in Forecasting 16.3.1 Providing Feedback 16.3.2 Providing Advice 16.3.3 Restrictiveness 16.3.4 Decomposition 16.3.5 Correcting Forecasts 16.3.6 Other Improvement Strategies 16.3.7 Improving Forecasts from Groups 16.3.8 Integrating Judgment with Algorithm-based Forecasts 16.4 Conclusions References 16.2.2 17 Input Uncertainty in Stochastic Simulation Russell R. Barton, Henry Lam, and Eunhye Song 17.1 Introduction 17.2 Characterizing Input Uncertainty 17.3 Confidence Interval Construction and Variance Estimation 17.3.1 Parametric Methods 17.3.2 Nonparametric Methods 17.3.3 Empirical Likelihood 17.4 Other Aspects 17.4.1 Bias Estimation 17.4.2 Online Data 17.4.3 Data Collection vs Simulation Expense 17.4.4 Model Calibration and Inverse Problems 17.5 Simulation Optimization under Input Uncertainty 17.5.1 Selection of the Best under Input Uncertainty 17.5.2 Global Optimization under Input Uncertainty 17.5.3 Online Optimization with StreamingInput Data 17.6 FutureResearch Directions References 547 553 553 554 555 555 557 558 559 560 562 563 573 573 575 576 578 584 596 597 598 598 598 599 599 602 608 609 610 611
Contents 18 19 xxiii Fuzzy multi-attribute decision-making: Theory, methods and Applications 621 Zeshui Xu and Shen Zhang 18.1 Introduction 18.2 Literature Review of Fuzzy MADM 18.3 Fuzzy MADM Based on the Information Integration 18.3.1 Information Integration and Fuzzy MADM 18.3.2 Fuzzy Aggregation Operators 18.3.3 Supplementary Instruction 18.4 Fuzzy Measures and MADM 18.4.1 Fuzzy Measures 18.4.2 Application of Fuzzy Measures in MADM 18.5 Fuzzy Preference Relations and MADM 18.5.1 Fuzzy Preference Relations 18-5-2 Fuzzy Analytic Hierarchy Process 18.6 Some Other Classical Synthetic Fuzzy MADM Methods 641 18.6.1 Fuzzy PROMETHEE 18.6.2 Fuzzy ELECTRE 18.6.3 Hesitant Fuzzy TODIM 18.7 Prospects for Future Research Directions References Importance Measures in Reliability Engineering: An Introductory Overview Shaomin Wu and Frank Coolen 19.1 Introduction 19.2 Basic Concepts of Reliability 19.2.1 Reliability Function 19.2.2 System Reliability Analysis 19.3 Importance Measures 19.3.1 Technology-Based Measures 19.3.2 Utility-Based Importance Measures 19.3.3 Importance Measures Based on the Survival Signature 19.3.4 Importance Measures and Some Important Concepts 19.4 Information Needed for Importance Measures 19-5 Applications of Importance Measures 19.6 Concluding Remarks References 621 624 627 627 628 631 632 632 635 638 638 639 641 643 644 646 648 659 659 660 660 661 663 663 666 666 667 669 670 672 673
xxiv Contents 20 Queues with Variable Service Speeds: Exact Results and Scaling Limits 675 Moeko Yajima and Tuan Phung-Duc 20.1 Introduction 675 20.2 Queues with Vacation/setup time 676 20.2.1 Vacation Models 676 20.2.2 Models with Workload /Queue-Length-Dependent Service 678 20.2.3 Models with Setup Time 679 20.2.4 Open Problems 684 20.3 Infinite-Server Queues with Changeable Service 684 20.3.1 Stability Condition 684 20.3.2 Exact Analysis 686 20.3.3 Limit Analysis 688 References 691 21 Forecasting and its Beneficiaries Bahman Rostami-Tabar and John E. Boylan 21.1 Background 21.2 Forecasting for Social Good 21.3 Barriers to Sharing the Benefits of Forecasting 21.3.1 Access to Resources 21.3.2 Access to Expertise 21.3.3 Communication Issues 21.4 More Effective Communications 21.4.1 Forecast Producers 21.4.2 Commercial Software Developers 21.4.3 Forecasting Academics 21.5 Democratising forecasting 21.5.1 Data Science Initiatives 21.5.2 Democratising Forecasting Initiative 21.5.3 Benefits and Impact of Democratising Forecasting 21.5.4 Challenges in Delivering Democratising Forecasting Workshops 21.5.5 Limitations of the Democratising Forecasting Initiative 21.5.6 Future Agenda for Democratising Forecasting 21.6 Conclusions References 695 695 697 698 699 700 701 701 703 703 704 705 706 707 708 709 710 711 712 714
xxv Contents Part V 22 23 Problem Structuring and Behavioural OR Behavioural OR: Recent developments and future perspectives Martin Kune and Konstantinos И Katsikopoulos 22.1 Introduction 22.2 Recent Developments 22.2.1 Conceptual Framework of BOR 22.2.2 Comparison between BOR and BOM 22.2.3 Empirical Basis of BOR 22.2.4 Behavioural Science and BOR 22.3 Future Developments 22.3.1 BOR and AI 22.3.2 Theoretical and Methodological Resources for BOR 22.3.3 Education Resources for BOR 22.4 Conclusions References 721 721 722 722 723 724 725 726 726 727 730 730 731 Problem Structuring Methods: Taking Stock and Looking Ahead 735 L. Alberto Franco and Etienne A. J. A. Rouwette 23.1 Introduction 735 23.2 The Characteristics of PSMs 738 23.2.1 PSM Tools 741 23.2.2 PSM Process 743 23.3 The Use of PSMs in Practice 744 23.3.1 Sample of PSM applications 748 23.3.2 Evidence of Use and Applications 751 23.4 PSM Products, Mechanisms, Supporting Evidence 754 23.4.1 Claimed Products and Facilitative Mechanisms 754 23.4.2 Supporting Empirical Evidence 760 23.5 The Future of PSMs 763 23.5.1 Becoming a Competent PSM Practitioner 763 23.5.2 Becoming a Competent PSM Researcher 766 23.6 Conclusion 768 References 769
xxvi 24 Contents Are PSMs Relevant in a Digital Age? Towards an Ethical Dimension 781 Isabella Μ. Lami and Leroy White 24.1 Introduction 24.2 Where Are We Now? 24.2.1 What Is the Nature of the Problem Today? 24.2.2 PSMs Today 24.2.3 So What’s the Issue? 24.3 The Ethical Boundary 24.3.1 Case Vignette: Comparison of Two PSMs’ Remote Applications 790 24.4 Conclusions References ’* Part VI 25 26 781 782 783 784 785 788 795 795 Recent OR Applications Recent Advances in Big Data Analytics Daoji Li, Yinfei Kong, Zemin Zheng, andJianxin Pan 25.1 Introduction 25.2 Ultrahigh-Dimensional Data Analysis 25-2.1 Feature Screening 25.2.2 Interaction Screening for RegressionModels 25.2.3 Interaction Screening for Classification 25.3 Massive Data Analysis 25.3.1 Divide-and-Conquer Methods 25.3.2 Subsampling Methods ' 25.4 Summary and Discussion References OR/MS Models for the Humanitarian-Business Partnership 835 Ali Ghavamifar and S. Ali Torabi 26.1 Introduction to Humanitarian Logistics 26.2 Humanitarian Supply Chains and Their Challenges 26.3 The Incentives of Partnership During the Disaster Phases 840 26.4 Literature Review 26.4.1 Relief Procurement 26.5 Relief Warehousing, Transportation, andDistribution 26.6 Gap Analysis of Extant Literature 805 805 807 808 809 813 816 816 820 828 828 835 838 841 841 847 849
xxvii Contents 26.7 A Mathematical Model for Humanitarian-Business Partnership 26.8 Conclusion and Suggestions References 27 28 850 854 854 Drones and Delivery Robots: Models and Applications to Last Mile Delivery Cheng Chen and Emrah Demir 27.1 Introduction 27.2 Literature Review 27.2.1 Drones: Last Mile Delivery Applications 27.2.2 Delivery Robots: Last Mile Delivery Applications 27.3 Problem Description 2 7.3.1 The Estimation of Emissions 2 7.3.2 Mathematical Formulation 2 7.3.3 Solution Methodology 27 .Յ.4 Initial Solution Generation 27 .Յ.5 The Adaptive Mechanism for Deciding the Use of Operators 27 .Յ.6 The Proposed Neighborhood Operators 27.4 Numerical Analyses 27.4.1 Instances and Parameters 27.4.2 Computational Results 27.4.3 Sensitivity Analysis 27.5 Conclusions References Evaluating the Quality of Radiation Therapy Treatment Plans Using Data Envelopment Analyis Matthias Ehrgott, Andrea Raith, Glyn Shentall, John Simpson, and Emma Stubington 28.1 Introduction 28.2 The DEA Model 28.2.1 Orientation and Returns to Scale 28.3 Feature Selection 28.3.1 Expert Opinions 28.3.2 Principal Component Analysis 28.3.3 Environmental Factors 28.4 Dealing with Uncertainty 28.4.1 Simulation 859 859 861 861 863 865 866 866 870 871 871 872 874 874 875 877 878 879 883 883 885 887 887 887 888 889 890 891
xxviii Contents 28.4.2 Uncertain DEA 28.5 Application in Practice 28.5.1 Informing the Treatment Planning Process 28.5.2 Integration in the Treatment Planning Process References Index 891 892 892 894 895 899 |
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| spelling | The Palgrave handbook of operations research Saïd Salhi, John Boylan (editors) Handbook of operations research Operations research Cham, Switzerland palgrave macmillan [2022] © 2022 xli, 905 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Operations Research (DE-588)4043586-6 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Operations Research (DE-588)4043586-6 s DE-604 Salhi, Saïd (DE-588)1140451723 edt Boylan, John (DE-588)1267857919 edt Erscheint auch als Online-Ausgabe 978-3-030-96935-6 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033692413&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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