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Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

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  • Nearchou, Andreas C.
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    Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.

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    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 129 (2011)
    Issue (Month): 2 (February)
    Pages: 242-250

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    Handle: RePEc:eee:proeco:v:129:y:2011:i:2:p:242-250
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    1. Kemmoé Tchomté, Sylverin & Gourgand, Michel, 2009. "Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems," International Journal of Production Economics, Elsevier, vol. 121(1), pages 57-67, September.
    2. Vergidis, K. & Tiwari, A. & Majeed, B. & Roy, R., 2007. "Optimisation of business process designs: An algorithmic approach with multiple objectives," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 105-121, September.
    3. Tsai, Chi-Yang & Yeh, Szu-Wei, 2008. "A multiple objective particle swarm optimization approach for inventory classification," International Journal of Production Economics, Elsevier, vol. 114(2), pages 656-666, August.
    4. Liu, Shixin & Tang, Jiafu & Song, Jianhai, 2006. "Order-planning model and algorithm for manufacturing steel sheets," International Journal of Production Economics, Elsevier, vol. 100(1), pages 30-43, March.
    5. Ding, Fong-Yuen & Zhu, Jin & Sun, Hui, 2006. "Comparing two weighted approaches for sequencing mixed-model assembly lines with multiple objectives," International Journal of Production Economics, Elsevier, vol. 102(1), pages 108-131, July.
    6. Gamberini, Rita & Grassi, Andrea & Rimini, Bianca, 2006. "A new multi-objective heuristic algorithm for solving the stochastic assembly line re-balancing problem," International Journal of Production Economics, Elsevier, vol. 102(2), pages 226-243, August.
    7. Nearchou, Andreas C., 2006. "Meta-heuristics from nature for the loop layout design problem," International Journal of Production Economics, Elsevier, vol. 101(2), pages 312-328, June.
    8. Scholl, Armin & Becker, Christian, 2006. "State-of-the-art exact and heuristic solution procedures for simple assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 666-693, February.
    9. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2008. "Assembly line balancing: Which model to use when," International Journal of Production Economics, Elsevier, vol. 111(2), pages 509-528, February.
    10. Scholl, Armin, 1995. "Balancing and sequencing of assembly lines," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 9690, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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