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A Hybrid Algorithm Based on Particle Swarm Optimization and Artificial Immune for an Assembly Job Shop Scheduling Problem

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  • Hui Du
  • Dacheng Liu
  • Mian-hao Zhang

Abstract

To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP). Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO) algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.

Suggested Citation

  • Hui Du & Dacheng Liu & Mian-hao Zhang, 2016. "A Hybrid Algorithm Based on Particle Swarm Optimization and Artificial Immune for an Assembly Job Shop Scheduling Problem," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, August.
    • Handle: RePEc:hin:jnlmpe:3938679
    DOI: 10.1155/2016/3938679
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