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Evolutionary Computation in Scheduling

Author Amir H. Gandomi , Ali Emrouznejad , Mo M. Jamshidi , Kalyanmoy Deb , Iman Rahimi
Release Date: 2020/05/01
ISBN: 9781119573845
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Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems

This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches.

Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book:

  • Provides a representative sampling of real-world problems currently being tackled by practitioners
  • Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence
  • Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems

Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.

Table of Contents

  1. Cover
  2. List of Contributors
  3. Editors’ Biographies
  4. Preface
  5. Acknowledgments
  6. 1 Evolutionary Computation in Scheduling
    1. 1.1 Introduction
    2. 1.2 Analysis
    3. 1.3 Scientometric Analysis
    4. 1.4 Conclusion and Direction for Future Research
    5. References
  7. 2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems
    1. 2.1 Introduction
    2. 2.2 Ant Colony Optimization
    3. 2.3 Review of Recent Articles
    4. 2.4 Results Analysis
    5. 2.5 Conclusion
    6. References
  8. 3 Advanced Ant Colony Optimization in Healthcare Scheduling
    1. 3.1 History of Ant Colony Optimization
    2. 3.2 Introduction to ACO as a Metaheuristic
    3. 3.3 Other Advanced Ant Colony Optimization
    4. 3.4 Introduction to Multi‐Objective Ant Colony Optimization (MOACO)
    5. 3.5 Keywords Analysis for Application of ACO in Scheduling
    6. 3.6 Application of Bi‐Objective Ant Colony Optimization in Healthcare Scheduling
    7. References
  9. 4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence
    1. 4.1 Introduction
    2. 4.2 Problem Formulation
    3. 4.3 SI in the TS Problem
    4. 4.4 Dynamic Topology: Binary Heap DPSO (BHDPSO) in Scheduling Problem
    5. 4.5 Evaluation Metrics
    6. 4.6 Simulation Results
    7. 4.7 Real‐Time Application – Smart Traffic System
    8. 4.8 Conclusion
    9. References
  10. 5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization
    1. 5.1 Introduction and Problem Statement Formulation
    2. 5.2 CUAS Scheme Using GA
    3. 5.3 Selection of Optimum Quantization Thresholds for MU‐MIMO Systems Using GA
    4. 5.4 Selection of Optimum Quantization Thresholds for MU MIMO‐OFDM Systems Using GA
    5. 5.5 Scheduling for MIMO Systems Using PSO
    6. 5.6 Conclusion
    7. References
  11. 6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure
    1. 6.1 Introduction and Literature Review
    2. 6.2 Proposed Problem
    3. 6.3 Red Deer Algorithm (RDA)
    4. 6.4 Computational Results
    5. 6.5 Conclusion and Future Works
    6. References
  12. 7 Application of Sub‐Population Scheduling Algorithm in Multi‐Population Evolutionary Dynamic Optimization
    1. 7.1 Introduction
    2. 7.2 Literature Review
    3. 7.3 Problem Statement
    4. 7.4 Theory of Learning Automata
    5. 7.5 Scheduling Algorithms
    6. 7.6 Application of the SPS Algorithms on a Multi‐Population Evolutionary Dynamic Optimization Method
    7. 7.7 Computational Experiments
    8. 7.8 Conclusions
    9. Acknowledgments
    10. References
  13. 8 Task Scheduling in Cloud Environments
    1. 8.1 Introduction
    2. 8.2 Physical Topology of Cloud
    3. 8.3 Research Methodology
    4. 8.4 Results
    5. 8.5 Research Gaps and Future Directions
    6. 8.6 Conclusion
    7. References
  14. 9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms
    1. 9.1 Introduction
    2. 9.2 Related Works
    3. 9.3 Disassembly Model
    4. 9.4 Optimization Objective
    5. 9.5 Bees Algorithm
    6. 9.6 Experiments and Results
    7. 9.7 Conclusion
    8. Acknowledgments
    9. A. Appendix A
    10. References
  15. 10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies
    1. 10.1 Introduction
    2. 10.2 Modeling
    3. 10.3 Fireworks Algorithm
    4. 10.4 Simulation Results
    5. 10.5 Conclusion
    6. Acknowledgment
    7. References
  16. Index
  17. End User License Agreement