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Decentralized Optimization with Independent Decision Makers [Mīkstie vāki]

  • Formāts: Paperback / softback, 184 pages, height x width x depth: 229x152x10 mm, weight: 254 g
  • Izdošanas datums: 06-Nov-2008
  • Izdevniecība: VDM Verlag Dr. Mueller E.K.
  • ISBN-10: 3639025792
  • ISBN-13: 9783639025798
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  • Cena: 74,10 €
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Decentralized Optimization with Independent Decision MakersPalielināt
  • Formāts: Paperback / softback, 184 pages, height x width x depth: 229x152x10 mm, weight: 254 g
  • Izdošanas datums: 06-Nov-2008
  • Izdevniecība: VDM Verlag Dr. Mueller E.K.
  • ISBN-10: 3639025792
  • ISBN-13: 9783639025798
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783639025798.html
Following the advances in electronics and communications technology in the last three decades, a new paradigm for large-scale dynamic systems emerged. In this paradigm, groups ofindependent dynamic systems, such as unmanned air vehicles or spacecraft, act as a cooperative unit for a diverse set of applications in remote sensing, exploration, and imaging. These systems have been envisioned to provide highly flexible andreconfigurable structures that use individual autonomy to respond to changing environments and operations. The main aim of this work has been to design methods and algorithms to enable efficient operations for such large-scale dynamic systems when a centralized decision-maker cannot or doesnot exist. Towards this end, a decentralized optimization method and a coordination algorithm have been developed. This methodology is applied to decentralized coordination problems from the aerospace and t

he operations research fields.

Gokhan Inalhan, Ph.D. : Stanford University, Aeronautics and Astronautics 2004. Ph. D. Minor : Engineering Economic Systems and Operations Research. Assistant Professor at Istanbul Technical University, Faculty of Aeronautics and Astronautics.
Preface v
1 Introduction
1
1.1 Background and Motivation
1
1.1.1 Unmanned Air Vehicle Group Coordination
2
1.1.2 Inventory Control in Supply-Chains
7
1.1.3 Approach
12
1.2 Previous and Related Work
13
1.2.1 Distributed optimization, decomposition methods and numerical optimization
13
1.2.2 Economics literature, game theoretic solutions and multi-agent systems
15
1.2.3 Multiple vehicle control and coordination
16
1.3 Contributions
18
1.4 Outline
19
2 Inventory Management in Supply-Chains
23
2.1 Overview
23
2.2 Supply-Chain Model and Problem
25
2.2.1 Subcontractor's Perspective
27
2.2.2 Main Contractor's Perspective
30
2.2.3 Global Optimization Problem
32
2.2.4 Decentralized Optimization
33
3 UAV Safety Zone Coordination
39
3.1 Overview
39
3.2 Mathematical Model
43
3.2.1 The independent local optimization problem of each UAV
45
3.2.2 The decentralized optimization framework
48
4 Centralized and Decentralized Optimization
51
4.1 Overview
51
4.2 Summary of this chapter's results
57
4.3 Centralized Optimization
59
4.3.1 Motivation: Dynamic Systems with Multiple Decision Makers and Coupling Constraints
59
4.3.2 Centralized Optimization Problem
61
4.3.3 Illustrative example: Pareto-optimal solutions for the GN&C manufacturing supply-chain
62
4.4 Decentralized Optimization
67
4.4.1 Decentralized optimal solutions: An illustrative example
70
4.5 Summary
72
5 Penalty-based Method and Iterative Algorithm
73
5.1 Penalty Methods for Decentralized Optimization
73
5.2 Coordination Assumptions
76
5.3 Global Cost Function
76
5.4 Emulation of Block Iterative Methods via Sequential Subsystem Optimizations, and Convergence of the Global Metric
78
5.5 Illustration of the Algorithm
82
5.6 An Illustrative Example
90
6 First and Second Order Optimality Conditions
97
6.1 Overview
97
6.2 Assumptions and Solution Type
100
6.3 Differentiable Inexact Penalty Format
103
6.4 Global Convergence to Always Feasible Solutions
105
6.5 f-Optimality for Decentralized Optimization
107
6.6 Second-Order Optimality Conditions
107
7 Analytic Supply-Chain Example
113
7.1 Supply-chain with multiple levels
113
7.2 The decentralized optimization formulation
119
7.3 The feasibility analysis
121
7.4 Optimality Analysis
123
8 Numeric UAV Safety Zone Coordination Example
125
8.1 Numerical Implementation
125
8.1.1 The first example (Scenario A)
130
8.1.2 The second example
131
8.1.3 The third set of examples (Scenarios B, C, D, E)
131
8.1.4 Extension to Multiple Vehicle Routing Problems
134
8.1.5 Extension to Moving-Time Horizon
136
9 Conclusions & Future Work
141
9.1 Summary of Contributions
141
9.1.1 Decentralized optimization model
142
9.1.2 Penalty-based method and coordination algorithm for decentralized optimization
142
9.1.3 Analysis of convergence, feasibility and optimality
143
9.2 Future Work
143
10 Appendix - Proofs 147
Bibliography 159