Evolutionary Computation in Scheduling 1st edition by Amir Gandomi, Ali Emrouznejad, Mo Jamshidi, Kalyanmoy Deb, Iman Rahimi 1119573876 9781119573876

Evolutionary Computation in Scheduling 1st edition by Amir H Gandomi, Ali Emrouznejad, Mo M Jamshidi, Kalyanmoy Deb, Iman Rahimi – Ebook PDF Instant Download/DeliveryISBN:  1119573876, 9781119573876 

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Product details:

ISBN-10 :  1119573876 

ISBN-13 :  9781119573876

Author:  Amir H Gandomi, Ali Emrouznejad, Mo M Jamshidi, Kalyanmoy Deb, Iman Rahimi 

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 1st Table of contents:

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

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Tags: Evolutionary, Computation, Scheduling, Amir Gandomi, Ali Emrouznejad, Mo Jamshidi, Kalyanmoy Deb, Iman Rahimi