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E-raamat: Best Matching Theory & Applications

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Mismatch or best match? This book demonstrates that best matching of individual entities to each other is essential to ensure smooth conduct and successful competitiveness in any distributed system, natural and artificial. Interactions must be optimized through best matching in planning and scheduling, enterprise network design, transportation and construction planning, recruitment, problem solving, selective assembly, team formation, sensor network design, and more. Fundamentals of best matching in distributed and collaborative systems are explained by providing:





§ Methodical analysis of various multidimensional best matching processes





§ Comprehensive taxonomy, comparing different best matching problems and processes





§ Systematic identification of systems hierarchy, nature of interactions, and distribution of decision-making and control functions





§ Practical formulation of solutions based on a library of best matching algorithms and protocols, ready for direct applications and apps development.





Designed for both academics and practitioners, oriented to systems engineers and applied operations researchers, diverse types of best matching processes are explained in production, manufacturing, business and service, based on a new reference model developed at Purdue University PRISM Center: The PRISM Taxonomy of Best Matching. The book concludes with major challenges and guidelines for future basic and applied research in the area of best matching.
1 Introduction: Best Matching and Best Match
1(18)
1.1 What Is Best Matching?
1(3)
1.2 Definitions and Scope
4(5)
1.2.1 Distributed Systems
6(2)
1.2.2 Collaboration Versus Competition
8(1)
1.3 Best Matching in Practice
9(3)
1.4 Summary
12(7)
References
14(5)
2 The PRISM Taxonomy of Best Matching
19(24)
2.1 Framework
19(11)
2.1.1 D1: Sets of Individuals
21(1)
2.1.2 D2: Matching Conditions
22(4)
2.1.3 D3: Matching Criteria
26(3)
2.1.4 D+: Time or Progression
29(1)
2.1.5 The Prismatic Structure of the PRISM Taxonomy
30(1)
2.2 Four Examples of the PRISM Taxonomy Application
30(7)
2.2.1 Balancing Collaborative Assembly Lines (M : 1/RC, PR, RS/ -- --, WS)
30(1)
2.2.2 Part Pairing for Concurrent Loading-Machining (1:1//-- OS)
31(2)
2.2.3 Dynamic Teaming with Interdependent Preferences (M : 1/RC, IP/ +, OS/DI, ES)
33(2)
2.2.4 Location-Allocation Decisions in CNO (1: M: M/RC, PR, RS/ + --, WS)
35(2)
2.3 Summary
37(6)
References
41(2)
3 Mathematical Models of Best Matching
43(20)
3.1 Why Mathematical Modeling for Best Matching?
43(2)
3.2 D1. Sets
45(4)
3.2.1 One-to-One Matching
46(1)
3.2.2 Generalized Matching
47(1)
3.2.3 Multi-Dimensional Matching
48(1)
3.3 D2. Conditions
49(8)
3.3.1 Resource-Constrained Matching
49(2)
3.3.2 Matching with Precedence Relations
51(1)
3.3.3 Matching with Resource Sharing
51(2)
3.3.4 Matching with Interdependent Preferences
53(3)
3.3.5 Layered Matching
56(1)
3.4 D3. Criteria
57(2)
3.5 D+. Static Versus Dynamic Matching
59(1)
3.6 Summary
60(3)
References
62(1)
4 Distributed Decision-Making and Best Matching
63(18)
4.1 Single Versus Multiple Decision-Makers
63(2)
4.2 Distribution of Decisional Abilities
65(9)
4.2.1 Example 1: Intelligent Warehouse Management Systems
68(2)
4.2.2 Example 2: Precision Agriculture
70(2)
4.2.3 Alternative Configurations---Advantages and Limitations
72(2)
4.3 Nature of Interactions
74(2)
4.4 Summary
76(5)
References
78(3)
5 Static and Centralized Matching
81(44)
5.1 Motivation for Using Algorithms
81(2)
5.2 Heuristics and Exact Algorithms
83(19)
5.2.1 Hungarian Method
83(3)
5.2.2 Deferred Acceptance Algorithm
86(3)
5.2.3 Lagrangian Relaxation Method
89(9)
5.2.4 Branch-and-Bound Method
98(4)
5.3 Metaheuristics
102(16)
5.3.1 Genetic Algorithm (GA)
103(4)
5.3.2 Greedy Randomized Adaptive Search Procedure (GRASP)
107(3)
5.3.3 Ant Colony Optimization (ACO)
110(4)
5.3.4 Tabu Search
114(4)
5.4 Summary
118(7)
References
122(3)
6 Dynamic and Distributed Matching
125(42)
6.1 Why Are Static and Centralized Algorithms not Always Sufficient?
125(2)
6.2 Real-Time Optimization
127(15)
6.2.1 Periodic Review Method
129(5)
6.2.2 Continuous Review Method
134(8)
6.3 Distributed Control
142(11)
6.3.1 Multi-agent Systems
142(4)
6.3.2 Interaction Protocols
146(7)
6.4 The "AI" Challenges (Artificial Intelligence; Analytics and Informatics)
153(6)
6.4.1 Artificial Intelligence
153(4)
6.4.2 Analytics and Informatics
157(2)
6.5 Summary
159(8)
References
163(4)
7 Extended Examples of Best Matching
167(54)
7.1 Understanding Through Analogy
167(3)
7.2 E1: Collaborative Supply Networks (M: 1/RC, RS/--, OS/DI)
170(12)
7.2.1 Mathematical Formulation
172(2)
7.2.2 D+: Task Administration Protocol
174(5)
7.2.3 Measured Impact
179(1)
7.2.4 Discussion
180(2)
7.3 E2: Collaborative Assembly Lines (M: 1/RC, PR, RS/-- -- --, GP/DI)
182(12)
7.3.1 Mathematical Formulation
184(4)
7.3.2 D+: Collaborative Multi-agent System
188(4)
7.3.3 Measured Impact
192(1)
7.3.4 Discussion
192(2)
7.4 E3: Clustering with Interdependent Preferences (M: 1/RC, IP/ +, OS/ES)
194(8)
7.4.1 Optimal Clustering: Genetic Algorithm (GA)
196(3)
7.4.2 D+: Association/Dissociation
199(2)
7.4.3 Measured Impact
201(1)
7.4.4 Discussion
201(1)
7.5 E4: Collaborative Service Enterprises (1:M:M/RC, RS/ + --, WS)
202(9)
7.5.1 Mathematical Formulation
204(2)
7.5.2 Optimization: Tabu Search
206(4)
7.5.3 Measured Impact
210(1)
7.6 Summary
211(10)
Appendix 1 Notation
213(4)
References
217(4)
8 Frontiers in Best Matching
221(8)
8.1 Emerging Technologies Dealing with Best Matching
221(2)
8.1.1 Connected Robots
222(1)
8.1.2 Cloud Manufacturing
222(1)
8.2 Technical Challenges of Best Matching
223(3)
8.2.1 Efficient Computation and Communication
223(1)
8.2.2 Conflict and Error Detection and Prevention
224(1)
8.2.3 Incentives for Collaboration
224(1)
8.2.4 Data Availability and Reliability
225(1)
8.3 Summary
226(3)
References
228(1)
Index 229