Atjaunināt sīkdatņu piekrišanu

E-grāmata: Metaheuristics for Resource Deployment under Uncertainty in Complex Systems [Taylor & Francis e-book]

, , , (Uni of Florida, USA),
  • Formāts: 192 pages, 38 Tables, black and white; 42 Line drawings, black and white; 2 Halftones, black and white; 44 Illustrations, black and white
  • Izdošanas datums: 30-Sep-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003202653
Citas grāmatas par šo tēmu:
  • Taylor & Francis e-book
  • Cena: 128,96 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standarta cena: 184,22 €
  • Ietaupiet 30%
Metaheuristics for Resource Deployment under Uncertainty in Complex SystemsPalielināt
  • Formāts: 192 pages, 38 Tables, black and white; 42 Line drawings, black and white; 2 Halftones, black and white; 44 Illustrations, black and white
  • Izdošanas datums: 30-Sep-2021
  • Izdevniecība: CRC Press
  • ISBN-13: 9781003202653
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9781003202653
"This book analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used"--

This book analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided. Theories of modeling and solution techniques are used with uncertainty taken into account and real-world applications used.

The authors present modeling and metaheuristics for solving resource deployment problems under uncertainty while the models deployed are related to stochastic programming, robust optimization, fuzzy programming, risk management, and single/multi-objective optimization. The resources are heterogeneous and can be sensors and actuators providing different tasks. Both separate and cooperative coverage of the resources are analyzed. Previous research has generally dealt with one type of resource and considers static and deterministic problems so the book breaks new ground in its analysis of cooperative coverage with heterogeneous resources and the uncertain and dynamic properties of these resources using metaheuristics.

This book will benefit researchers, professionals, academics, and graduate students in related areas to better understand the theory and application of resource deployment problems and theories of uncertainty, including problem formulations, assumptions, and solution methods.



This book analyzes how to set locations for the deployment of resources to incur the best performance at the lowest cost. Resources can be static nodes and moving nodes while services for a specific area or for customers can be provided.

Preface xi
Acknowledgments xv
Author Bios xvii
Chapter 1 Introduction 1(28)
1.1 Applications Of Node Deployment Problem
1(8)
1.1.1 Unmanned Systems
1(2)
1.1.2 Wireless Sensor Networks
3(1)
1.1.3 Healthcare
4(2)
1.1.4 Public Sectors
6(1)
1.1.5 Railway Network Design
6(2)
1.1.6 Distributed Simulation Systems
8(1)
1.2 Fundamental Issues Of Node Deployment Problem
9(3)
1.2.1 Task
10(1)
1.2.2 Node
11(1)
1.2.3 Environment
11(1)
1.3 Research Progress Of Node Deployment Modeling
12(8)
1.3.1 Deployment Space
12(3)
1.3.1.1 Candidate Locations
12(2)
1.3.1.2 Deployment Formation
14(1)
1.3.2 Constraints
15(1)
1.3.3 Objective Functions
16(4)
1.3.3.1 Node Deployment In Wireless Sensor Networks
16(1)
1.3.3.2 Node Deployment In Air Defense
17(3)
1.3.3.3 Other Types Of Optimization Objective
20(1)
1.4 Research Progress Of Node Deployment Methods
20(5)
1.4.1 Encoding
21(1)
1.4.2 Constraints Handling
21(1)
1.4.3 Multi-Objective Handling
21(1)
1.4.4 Algorithms
22(9)
1.4.4.1 Exact Algorithm
22(1)
1.4.4.2 Metaheuristic Algorithm
23(2)
1.5 Main Issues And Challenges
25(2)
1.6 Book Outline
27(2)
Chapter 2 Stochastic Node Deployment For Area Coverage Problem 29(16)
2.1 Introduction
29(2)
2.2 Problem Formulation
31(4)
2.2.1 Detection Models
31(1)
2.2.1.1 Binary Detection Model
31(1)
2.2.1.2 Probabilistic Detection Model
32(1)
2.2.2 Network Model
32(1)
2.2.3 Problem Statement
33(1)
2.2.4 NP-Hardness Proof
34(1)
2.3 Solution Algorithms
35(4)
2.3.1 D-VFCPSO
35(3)
2.3.2 Other PSO-Based Algorithm For Area Coverage Problem
38(1)
2.3.3 Complexity Analysis
39(1)
2.4 Experiments And Discussion
39(2)
2.4.1 Test Instances
40(1)
2.4.2 Parameter Setting
40(1)
2.4.3 Analysis Of Results
41(1)
2.5 Conclusion
41(4)
Chapter 3 Stochastic Dynamic Node Deployment For Target Coverage Problem 45(30)
3.1 Introduction
46(1)
3.2 Problem Formulation
47(3)
3.2.1 Mathematical Model
49(1)
3.2.2 Scenario-Based Model Reformulation
49(1)
3.3 Solution Algorithms
50(6)
3.3.1 NSGA-II
50(2)
3.3.2 MOPSO
52(3)
3.3.2.1 Personal Best Selection
52(1)
3.3.2.2 Non-Dominated Solutions Maintaining And Global Best Selection
53(1)
3.3.2.3 Diversity Maintaining
53(2)
3.3.3 Complexity Analysis
55(1)
3.4 Experiments And Discussion
56(18)
3.4.1 Test Instances
56(1)
3.4.2 Performance Metrics
57(2)
3.4.3 Parameter Turning
59(1)
3.4.4 Analysis Of Results
60(14)
3.5 Conclusion
74(1)
Chapter 4 Robust Node Deployment For Cooperative Coverage Problem 75(40)
4.1 Introduction
76(2)
4.2 Problem Formulation
78(10)
4.2.1 The Deterministic And Uncertain Two-Level Cooperative Set Covering Problem
78(6)
4.2.1.1 Two-Level Cooperative Set Covering Problem
78(1)
4.2.1.2 Generalized Uncertain Two-Level Cooperative Set Covering Problem
79(5)
4.2.2 Modeling The Robust Uncertain Two-Level Cooperative Set Covering Problem
84(4)
4.2.2.1 Compact Formulation Of The RUTLCSCP
87(1)
4.3 Solution Algorithms
88(13)
4.3.1 Dealing With Subproblem
89(3)
4.3.2 Rule-Based Heuristic For RUTLCSCP
92(4)
4.3.2.1 Processing Procedure
94(1)
4.3.2.2 Complexity Analysis Of MRBCH-K
95(1)
4.3.3 Proposed SaDE For RUTLCSCP
96(5)
4.3.3.1 Encoding
97(1)
4.3.3.2 Constraints Handling
97(3)
4.3.3.3 Complexity Analysis Of SaDE
100(1)
4.4 Experiments And Discussion
101(12)
4.4.1 Test Instances
102(1)
4.4.2 Analysis Of Results
102(15)
4.4.2.1 Solving RUTLCSCP-LA-RC Through CPLEX
102(3)
4.4.2.2 Comparisons Of MRBCH-K With Different K
105(4)
4.4.2.3 Comparisons Of SaDE And Its Variants
109(1)
4.4.2.4 Comparisons On RUTLCSCP
109(4)
4.5 Conclusion
113(2)
Chapter 5 Fuzzy Node Deployment For Cooperative Coverage Problem 115(24)
5.1 Introduction
116(1)
5.2 Problem Formulation
117(6)
5.2.1 Fuzzy Conditional Value-At-Risk
118(1)
5.2.2 Mathematical Model
118(3)
5.2.3 Some Properties On CVAR-FTLCNDP
121(1)
5.2.4 Linear Approximation Of CVAR-FTLCNDP
122(1)
5.3 Solution Algorithms
123(7)
5.3.1 Fuzzy Simulation
124(2)
5.3.2 Improved Decomposition-Based Multi-Objective Evolutionary Algorithms
126(4)
5.3.2.1 Encoding
126(1)
5.3.2.2 Updating Of Individuals
127(3)
5.3.2.3 Complexity Analysis
130(1)
5.4 Experiments And Discussion
130(6)
5.4.1 Performance Metrics
131(1)
5.4.2 Analysis Of Results
131(10)
5.4.2.1 Case Study 1
131(3)
5.4.2.2 Case Study 2
134(2)
5.5 Conclusion
136(3)
Chapter 6 Simulation-Based Evaluation Analysis Of Node Deployment Under Risk Preference 139(28)
6.1 Introduction
139(2)
6.2 Simulation-Based Evaluation Analysis Of Worst-Case CVAR Node Deployment
141(7)
6.2.1 Uncertain Initial Position Of Penetration Paths
142(1)
6.2.2 Penetration Paths Under Uncertainty
143(1)
6.2.3 Scenario-Based Simulation
144(4)
6.2.4 Evaluation Model With Decision Makers' Risk Preference
148(1)
6.3 Experiments And Discussion
148(16)
6.3.1 Case Study 1: Deployment Of Sensor Nodes
148(5)
6.3.2 Case Study 2: Deployment Of Weapon Nodes
153(2)
6.3.3 Case Study 3: Cooperative Deployment Of Sensor And Weapon Nodes
155(9)
6.4 Conclusion
164(3)
Chapter 7 Overview And Future Directions 167(4)
Bibliography 171(18)
Index 189
Shuxin Ding is currently an assistant researcher with the Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited. His current research interests include railway scheduling, evolutionary computation, multi-objective optimization, and optimization under uncertainty.

Chen Chen is currently a professor with the School of Automation, Beijing Institute of Technology. Her current research interests include complicated systems, multi-objective optimization, and distributed simulation.

Qi Zhang is currently a chief researcher of China Academy of Railway Sciences Corporation Limited, and a leader in railway technical expertise. His research interests include railway signal and communication, automatic train operation, train operation control, intelligent dispatching, and cooperative control of multiple trains.

Bin Xin is currently a professor with the School of Automation, Beijing Institute of Technology. His current research interests include search and optimization, evolutionary computation, unmanned systems, and multi-agent systems.

Panos M. Pardalos is a Distinguished Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of the Biomedical Engineering and Computer Science & Information & Engineering departments. He has published over 500 journal papers and edited/authored over 200 books. He is one of the most cited authors and has graduated 65 Ph.D. students so far.
Permanent link: https://www.kriso.lv/db/9781003202653_pe.html
Keywords: