Metaheuristics for Resource Deployment under Uncertainty in Complex Systems |
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| Author:
| Ding, Shuxin
Chen, Chen
Zhang, Qi
Xin, Bin
Pardalos, Panos M. |
| ISBN: | 978-1-003-20265-3 |
| Publication Date: | Sep 2021 |
| Publisher: | Taylor & Francis Group
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| Imprint: | CRC Press |
| Book Format: | Digital (delivered electronically) |
| List Price: | USD $99.95 |
Book Description:
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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...
More Description
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.