Metaheuristics for Resource Deployment under Uncertainty in Complex Systems

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. Cover Half Title Title Page Copyright Page Contents Preface Acknowledgments Author Bios CHAPTER 1: Introduction 1.1. APPLICATIONS OF NODE DEPLOYMENT PROBLEM 1.1.1. Unmanned Systems 1.1.2. Wireless Sensor Networks 1.1.3. Healthcare 1.1.4. Public Sectors 1.1.5. Railway Network Design 1.1.6. Distributed Simulation Systems 1.2. FUNDAMENTAL ISSUES OF NODE DEPLOYMENT PROBLEM 1.2.1. Task 1.2.2. Node 1.2.3. Environment 1.3. RESEARCH PROGRESS OF NODE DEPLOYMENT MODELING 1.3.1. Deployment Space 1.3.1.1. Candidate Locations 1.3.1.2. Deployment Formation 1.3.2. Constraints 1.3.3. Objective Functions 1.3.3.1. Node Deployment in Wireless Sensor Networks 1.3.3.2. Node Deployment in Air Defense 1.3.3.3. Other Types of Optimization Objective 1.4. RESEARCH PROGRESS OF NODE DEPLOYMENT METHODS 1.4.1. Encoding 1.4.2. Constraints Handling 1.4.3. Multi-Objective Handling 1.4.4. Algorithms 1.4.4.1. Exact Algorithm 1.4.4.2. Metaheuristic Algorithm 1.5. MAIN ISSUES AND CHALLENGES 1.6. BOOK OUTLINE CHAPTER 2: Stochastic Node Deployment for Area Coverage Problem 2.1. INTRODUCTION 2.2. PROBLEM FORMULATION 2.2.1. Detection Models 2.2.1.1. Binary Detection Model 2.2.1.2. Probabilistic Detection Model 2.2.2. Network Model 2.2.3. Problem Statement 2.2.4. NP-Hardness Proof 2.3. SOLUTION ALGORITHMS 2.3.1. D-VFCPSO 2.3.2. Other PSO-Based Algorithm for Area Coverage Problem 2.3.3. Complexity Analysis 2.4. EXPERIMENTS AND DISCUSSION 2.4.1. Test Instances 2.4.2. Parameter Setting 2.4.3. Analysis of Results 2.5. CONCLUSION CHAPTER 3: Stochastic Dynamic Node Deployment for Target Coverage Problem 3.1. INTRODUCTION 3.2. PROBLEM FORMULATION 3.2.1. Mathematical Model 3.2.2. Scenario-Based Model Reformulation 3.3. SOLUTION ALGORITHMS 3.3.1. NSGA-II 3.3.2. MOPSO 3.3.2.1. Personal Best Selection 3.3.2.2. Non-Dominated Solutions Maintaining and Global Best Selection 3.3.2.3. Diversity Maintaining 3.3.3. Complexity Analysis 3.4. EXPERIMENTS AND DISCUSSION 3.4.1. Test Instances 3.4.2. Performance Metrics 3.4.3. Parameter Turning 3.4.4. Analysis of Results 3.5. CONCLUSION CHAPTER 4: Robust Node Deployment for Cooperative Coverage Problem 4.1. INTRODUCTION 4.2. PROBLEM FORMULATION 4.2.1. The Deterministic and Uncertain Two-Level Cooperative Set Covering Problem 4.2.1.1. Two-Level Cooperative Set Covering Problem 4.2.1.2. Generalized Uncertain Two-Level Cooperative Set Covering Problem 4.2.2. Modeling the Robust Uncertain Two-Level Cooperative Set Covering Problem 4.2.2.1. Compact Formulation of the RUTLCSCP 4.3. SOLUTION ALGORITHMS 4.3.1. Dealing with Subproblem 4.3.2. Rule-Based Heuristic for RUTLCSCP 4.3.2.1. Processing Procedure 4.3.2.2. Complexity Analysis of MRBCH-k 4.3.3. Proposed SaDE for RUTLCSCP 4.3.3.1. Encoding 4.3.3.2. Constraints Handling 4.3.3.3. Complexity Analysis of SaDE 4.4. EXPERIMENTS AND DISCUSSION 4.4.1. Test Instances 4.4.2. Analysis of Results 4.4.2.1. Solving RUTLCSCP-LA-RC through CPLEX 4.4.2.2. Comparisons of MRBCH-k with Different k 4.4.2.3. Comparisons of SaDE and Its Variants 4.4.2.4. Comparisons on RUTLCSCP 4.5. CONCLUSION CHAPTER 5: Fuzzy Node Deployment for Cooperative Coverage Problem 5.1. INTRODUCTION 5.2. PROBLEM FORMULATION 5.2.1. Fuzzy Conditional Value-at-Risk 5.2.2. Mathematical Model 5.2.3. Some Properties on CVaR-FTLCNDP 5.2.4. Linear Approximation of CVaR-FTLCNDP 5.3. SOLUTION ALGORITHMS 5.3.1. Fuzzy Simulation 5.3.2. Improved Decomposition-Based Multi-Objective Evolutionary Algorithms 5.3.2.1. Encoding 5.3.2.2. Updating of Individuals 5.3.2.3. Complexity Analysis 5.4. EXPERIMENTS AND DISCUSSION 5.4.1. Performance Metrics 5.4.2. Analysis of Results 5.4.2.1. Case Study 1 5.4.2.2. Case Study 2 5.5. CONCLUSION CHAPTER 6: Simulation-Based Evaluation Analysis of Node Deployment under Risk Preference 6.1. INTRODUCTION 6.2. SIMULATION-BASED EVALUATION ANALYSIS OF WORST-CASE CVAR NODE DEPLOYMENT 6.2.1. Uncertain Initial Position of Penetration Paths 6.2.2. Penetration Paths under Uncertainty 6.2.3. Scenario-Based Simulation 6.2.4. Evaluation Model with Decision Makers’ Risk Preference 6.3. EXPERIMENTS AND DISCUSSION 6.3.1. Case Study 1: Deployment of Sensor Nodes 6.3.2. Case Study 2: Deployment of Weapon Nodes 6.3.3. Case Study 3: Cooperative Deployment of Sensor and Weapon Nodes 6.4. CONCLUSION CHAPTER 7: Overview and Future Directions Bibliography Index