Metaheuristics for Resource Deployment under Uncertainty in Complex Systems (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
192
Utgivningsdatum
2021-09-30
Förlag
CRC Press
Medarbetare
Zhang, Haili
Illustratör/Fotograf
black and white 38 Tables 42 Line drawings, black and white 2 Halftones black and white 44 Illu
Illustrationer
38 Tables, black and white; 42 Line drawings, black and white; 2 Halftones, black and white; 44 Illu
Dimensioner
234 x 156 x 13 mm
Vikt
477 g
Antal komponenter
1
Komponenter
52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam
ISBN
9781032065205

Metaheuristics for Resource Deployment under Uncertainty in Complex Systems

av Shuxin Ding, Chen Chen, Qi Zhang, Bin Xin, Panos Pardalos
Inbunden,  Engelska, 2021-09-30
1561
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Metaheuristics for Resource Deployment under Uncertainty in Complex Systems 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 help 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.
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Övrig information

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.

Innehållsförteckning

1. Introduction 2. Stochastic Node Deployment for Area Coverage Problem 3. Stochastic Dynamic Node Deployment for Target Coverage Problem 4. Robust Node Deployment for Cooperative Coverage Problem 5. Fuzzy Node Deployment for Cooperative Coverage Problem 6. Simulation-based Evaluation Analysis of Node Deployment under Risk Preference 7. Overview and Future Directions

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