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A Multi-Objective Particle Swarm for a Mixed-Model Assembly Line Sequencing

In: Operations Research Proceedings 2006

Author

Listed:
  • Seyed Mohammed Mirghorbani

    (University of Tehran)

  • Masoud Rabbani

    (University of Tehran)

  • Reza Tavakkoli-Moghaddam

    (University of Tehran)

  • Alireza R. Rahimi-Vahed

    (University of Tehran)

Abstract

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management. In this paper, three goals are considered for minimization; That is, total utility work, total production rate variation, and total setup cost. A hybrid multi-objective algorithm based on Particle Swarm Optimization (PSO) and Tabu Search (TS) is devised to solve the problem. The algorithm is then compared with three prominent multi-objective Genetic Algorithms and the results show the superiority of the proposed algorithm.

Suggested Citation

  • Seyed Mohammed Mirghorbani & Masoud Rabbani & Reza Tavakkoli-Moghaddam & Alireza R. Rahimi-Vahed, 2007. "A Multi-Objective Particle Swarm for a Mixed-Model Assembly Line Sequencing," Operations Research Proceedings, in: Karl-Heinz Waldmann & Ulrike M. Stocker (ed.), Operations Research Proceedings 2006, pages 181-186, Springer.
    • Handle: RePEc:spr:oprchp:978-3-540-69995-8_30
    DOI: 10.1007/978-3-540-69995-8_30
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    Cited by:

    1. Masood Fathi & María Jesús à lvarez & Victoria Rodríguez, 2016. "A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(2), pages 145-169.
    2. Ioanna Makarouni & John Psarras & Eleftherios Siskos, 2015. "Interactive bicriterion decision support for a large scale industrial scheduling system," Annals of Operations Research, Springer, vol. 227(1), pages 45-61, April.

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