A multi-objective particle swarm for a flow shop scheduling problem

A. R. Rahimi-Vahed () and S. M. Mirghorbani
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A. R. Rahimi-Vahed: University of Tehran
S. M. Mirghorbani: University of Tehran

Journal of Combinatorial Optimization, 2007, vol. 13, issue 1, No 6, 79-102

Abstract: Abstract Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

Keywords: Bi-criteria flow shop scheduling problem; Permutation flow shop; Multi-objective particle swarm; Multi-objective genetic algorithm (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10878-006-9015-7

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