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Table of Contents
Foreword; Preface; About the Book; Contents; 1 Introduction: Best Matching and Best Match; Abstract; 1.1 What Is Best Matching?; 1.2 Definitions and Scope; 1.2.1 Distributed Systems; 1.2.2 Collaboration Versus Competition; 1.3 Best Matching in Practice; 1.4 Summary; References; 2 The PRISM Taxonomy of Best Matching; Abstract; 2.1 Framework; 2.1.1 D1: Sets of Individuals; 2.1.2 D2: Matching Conditions; 2.1.3 D3: Matching Criteria; 2.1.4 D+: Time or Progression; 2.1.5 The Prismatic Structure of the PRISM Taxonomy; 2.2 Four Examples of the PRISM Taxonomy Application.
2.2.1 Balancing Collaborative Assembly Lines \left({M:1/RC, PR, RS/
, WS} \right) 2.2.2 Part Pairing for Concurrent Loading-Machining \left({1:1//
, OS} \right) ; 2.2.3 Dynamic Teaming with Interdependent Preferences \left({M:1/RC, IP/ {\, +\, }, OS/DI, ES} \right) ; 2.2.4 Location-Allocation Decisions in CNO \left({1:M:M/RC, PR, RS/ {\, +\, } \,
, WS} \right) ; 2.3 Summary; References; 3 Mathematical Models of Best Matching; Abstract; 3.1 Why Mathematical Modeling for Best Matching?; 3.2 D1. Sets; 3.2.1 One-to-One Matching; 3.2.2 Generalized Matching; 3.2.3 Multi-Dimensional Matching.
3.3 D2. Conditions3.3.1 Resource-Constrained Matching; 3.3.2 Matching with Precedence Relations; 3.3.3 Matching with Resource Sharing; 3.3.4 Matching with Interdependent Preferences; 3.3.4.1 One-to-One Matching with IP; 3.3.4.2 Many-to-One Matching with IP; 3.3.5 Layered Matching; 3.4 D3. Criteria; 3.5 D+. Static Versus Dynamic Matching; 3.6 Summary; References; 4 Distributed Decision-Making and Best Matching; Abstract; 4.1 Single Versus Multiple Decision-Makers; 4.2 Distribution of Decisional Abilities; 4.2.1 Example 1: Intelligent Warehouse Management Systems.
4.2.2 Example 2: Precision Agriculture4.2.3 Alternative Configurations-Advantages and Limitations; 4.3 Nature of Interactions; 4.4 Summary; References; 5 Static and Centralized Matching; Abstract; 5.1 Motivation for Using Algorithms; 5.2 Heuristics and Exact Algorithms; 5.2.1 Hungarian Method; 5.2.2 Deferred Acceptance Algorithm; 5.2.3 Lagrangian Relaxation Method; 5.2.4 Branch-and-Bound Method; 5.3 Metaheuristics; 5.3.1 Genetic Algorithm (GA); 5.3.2 Greedy Randomized Adaptive Search Procedure (GRASP); 5.3.3 Ant Colony Optimization (ACO); 5.3.4 Tabu Search; 5.4 Summary; References.
6 Dynamic and Distributed MatchingAbstract; 6.1 Why Are Static and Centralized Algorithms not Always Sufficient?; 6.2 Real-Time Optimization; 6.2.1 Periodic Review Method; 6.2.2 Continuous Review Method; 6.3 Distributed Control; 6.3.1 Multi-agent Systems; 6.3.2 Interaction Protocols; 6.4 The "AI" Challenges (Artificial Intelligence; Analytics and Informatics); 6.4.1 Artificial Intelligence; 6.4.2 Analytics and Informatics; 6.5 Summary; References; 7 Extended Examples of Best Matching; Abstract; 7.1 Understanding Through Analogy; 7.2 E1: Collaborative Supply Networks ({\varvec M}{:}\
1/{\varvec RC}, {\varvec RS}/
, {\varvec OS}/{\varvec DI}).
2.2.1 Balancing Collaborative Assembly Lines \left({M:1/RC, PR, RS/
, WS} \right) 2.2.2 Part Pairing for Concurrent Loading-Machining \left({1:1//
, OS} \right) ; 2.2.3 Dynamic Teaming with Interdependent Preferences \left({M:1/RC, IP/ {\, +\, }, OS/DI, ES} \right) ; 2.2.4 Location-Allocation Decisions in CNO \left({1:M:M/RC, PR, RS/ {\, +\, } \,
, WS} \right) ; 2.3 Summary; References; 3 Mathematical Models of Best Matching; Abstract; 3.1 Why Mathematical Modeling for Best Matching?; 3.2 D1. Sets; 3.2.1 One-to-One Matching; 3.2.2 Generalized Matching; 3.2.3 Multi-Dimensional Matching.
3.3 D2. Conditions3.3.1 Resource-Constrained Matching; 3.3.2 Matching with Precedence Relations; 3.3.3 Matching with Resource Sharing; 3.3.4 Matching with Interdependent Preferences; 3.3.4.1 One-to-One Matching with IP; 3.3.4.2 Many-to-One Matching with IP; 3.3.5 Layered Matching; 3.4 D3. Criteria; 3.5 D+. Static Versus Dynamic Matching; 3.6 Summary; References; 4 Distributed Decision-Making and Best Matching; Abstract; 4.1 Single Versus Multiple Decision-Makers; 4.2 Distribution of Decisional Abilities; 4.2.1 Example 1: Intelligent Warehouse Management Systems.
4.2.2 Example 2: Precision Agriculture4.2.3 Alternative Configurations-Advantages and Limitations; 4.3 Nature of Interactions; 4.4 Summary; References; 5 Static and Centralized Matching; Abstract; 5.1 Motivation for Using Algorithms; 5.2 Heuristics and Exact Algorithms; 5.2.1 Hungarian Method; 5.2.2 Deferred Acceptance Algorithm; 5.2.3 Lagrangian Relaxation Method; 5.2.4 Branch-and-Bound Method; 5.3 Metaheuristics; 5.3.1 Genetic Algorithm (GA); 5.3.2 Greedy Randomized Adaptive Search Procedure (GRASP); 5.3.3 Ant Colony Optimization (ACO); 5.3.4 Tabu Search; 5.4 Summary; References.
6 Dynamic and Distributed MatchingAbstract; 6.1 Why Are Static and Centralized Algorithms not Always Sufficient?; 6.2 Real-Time Optimization; 6.2.1 Periodic Review Method; 6.2.2 Continuous Review Method; 6.3 Distributed Control; 6.3.1 Multi-agent Systems; 6.3.2 Interaction Protocols; 6.4 The "AI" Challenges (Artificial Intelligence; Analytics and Informatics); 6.4.1 Artificial Intelligence; 6.4.2 Analytics and Informatics; 6.5 Summary; References; 7 Extended Examples of Best Matching; Abstract; 7.1 Understanding Through Analogy; 7.2 E1: Collaborative Supply Networks ({\varvec M}{:}\
1/{\varvec RC}, {\varvec RS}/
, {\varvec OS}/{\varvec DI}).