Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm

  • Authors:

  • Binggang Wang;Yunqing Rao;Xinyu Shao;Mengchang Wang

  • Affiliations:

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

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

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Abstract

This paper is concerned about how to optimize the input sequence for a mixed-model assembly line (MMAL) with limited intermediate buffers. Three optimization objectives are considered simultaneously: minimizing the total production rate variation, the total setup, and the total assembly cost. The mathematical model is presented by incorporating the three objectives. Since the problem is NP-hard, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA), is proposed for solving the model. The performance of the proposed algorithm is compared with a genetic algorithm for different-sized sequencing problems in MMALs that consist of different number of machines and different production plans. The computational results show that the proposed hybrid algorithm finds solutions with better quality and often needs a smaller number of generations to converge to a final stable state, especially in the case of large-sized problems.