Variable Neighborhood Genetic Algorithm for the Flexible Job Shop Scheduling Problems

  • Authors:

  • Guohui Zhang;Liang Gao;Xinyu Li;Peigen Li

  • Affiliations:

  • The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, China 430074;The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, China 430074;The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, China 430074;The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, China 430074

  • Venue:
  • ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
  • Year:
  • 2008

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Abstract

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. A Variable Neighborhood Genetic Algorithm (VNGA) is proposed for the problem with makespan criterion, consisting of a combination of the variable neighborhood search (VNS) and genetic algorithm (GA). Variable neighborhood search is adopted to improve the quality of individuals of GA before injecting them into the population and strengthen the local search ability. Two different neighborhood structures are used in the VNS. Representative flexible job shop scheduling benchmark problems are solved using the VNGA. Computational results show that the proposed algorithm is efficient and effective.