Intro
Preface
Contents
1 Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches
1.1 Introduction
1.2 Literature Review
1.3 Problem Description and Mathematical Formulation
1.3.1 Problem Description
1.3.2 Mathematical Formulation
1.4 Proposed Approaches
1.4.1 Iterative Multi-Objective Integer Linear Program (I-MOILP)
1.4.2 Adapted Archived Multi-Objective Simulated-Annealing (AMOSA)
1.4.3 Adapted Non Dominated Sorting Genetic Algorithm II (NSGA-II)
1.5 Experimental Results and Analyses

1.5.1 Experimental Scheme 1
1.5.2 Experimental Scheme 2
1.6 Conclusion
References
2 On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time
2.1 Introduction
2.2 Description of the Hybrid Flow Shop Problem
2.3 Resolution
2.3.1 Initialization Heuristics
2.3.2 Metaheuristics
2.4 Numerical Simulation
2.4.1 Simulation Instances
2.4.2 Experimental Results
2.5 Conclusion
References

3 A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion
3.1 Introduction
3.2 Problem Formulation
3.3 Energy-Efficient VBIH Algorithm
3.3.1 Initial Population
3.3.2 Energy-Efficient Block Insertion Procedure
3.3.3 Energy-Efficient Insertion Local Search
3.3.4 Energy-Efficient Uniform Crossover and Mutation
3.3.5 Archive Set
3.4 Computational Results
3.5 Conclusions
References
4 Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm
4.1 Introduction

4.2 Literature Review
4.3 Problem Formulation
4.4 Bi-Objective BGA
4.5 Computational Results
4.6 Conclusion
References
5 An Asynchronous Parallel Evolutionary Algorithm for Solving Large Instances of the Multi-objective QAP
5.1 Introduction
5.2 Related Works
5.3 The APM-MOEA Model
5.3.1 Global Search View of the Organizer
5.3.2 Asynchronous Communications
5.3.3 Control Islands
5.3.4 Local Search
5.4 Experimental Results
5.4.1 Performance Metrics
5.4.2 The GISMOO Algorithm
5.4.3 MQAP Instances
5.4.4 Experimental Conditions

5.4.5 Resolution of Small MQAP Instances
5.4.6 Resolution of Large MQAP Instances
5.5 Conclusion
References
6 Learning from Prior Designs for Facility Layout Optimization
6.1 Introduction
6.2 Related Work
6.3 Facility Layout Model
6.4 Similarity Model
6.4.1 Probabilistic Layout Model
6.4.2 Estimation
6.5 Similarity in Layout Optimization
6.6 Experiments
6.7 Discussion
References
7 Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm
7.1 Enhanced LSHADE with Semi-parameter Adaptation Hybrid with CMA-ES (ELSHADE-SPACMA)