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Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

Author

Listed:
  • Rong-Chang Chen
  • Jeanne Chen
  • Tung-Shou Chen
  • Chien-Che Huang
  • Li-Chiu Chen

Abstract

The permutation flowshop scheduling problem (PFSP) is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.

Suggested Citation

  • Rong-Chang Chen & Jeanne Chen & Tung-Shou Chen & Chien-Che Huang & Li-Chiu Chen, 2017. "Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, February.
    • Handle: RePEc:hin:jnlmpe:3630869
    DOI: 10.1155/2017/3630869
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