The purpose of this paper is to apply particle swarm optimization (PSO) a known combinatorial optimization algorithm to multi‐objective (MO) balancing of large assembly lines.
A novel approach based on PSO is developed to tackle the simple assembly line balancing problem (SALBP), a well‐known NP‐hard production and operations management problem. Line balancing is considered for two‐criteria problems utilizing cycle time and workload smoothing as performance criteria, as well as for three‐criteria problems involving the balance delay time of the line together with cycle time and workload smoothing. Emphasis is on seeking a set of diverse Pareto optimal solutions for the bi‐criteria SALBP.
Experiments carried out on multiple test problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing MO population heuristics show a quite promising higher performance for the proposed approach.
Artificial particles (potential solutions “flown” by PSO though hyperspace) are encoded to actual ALB solutions via a novel representation mechanism. A new scheme for generating and maintaining diverse Pareto ALB solutions is proposed. For the case of the two‐criteria ALBPs, the individual objectives are summed to a weighted combination with the weight coefficients being dynamically adapted using a novel weighted aggregation method. This weighted method can be applied on any bi‐criteria optimization problem.
Petropoulos, D. and Nearchou, A. (2011), "A particle swarm optimization algorithm for balancing assembly lines", Assembly Automation, Vol. 31 No. 2, pp. 118-129. https://doi.org/10.1108/01445151111117700Download as .RIS
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