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A hybrid particle swarm optimization approach for the job-shop scheduling problem

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Abstract

A new approximation algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling environment. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO combines local search (by self-experience) and global search (by neighboring experience), and possesses high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. By reasonably combining these two different search algorithms, we develop a general, fast and easily implemented hybrid optimization algorithm; we called the HPSO. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated by applying it to some benchmark job-shop scheduling problems. Comparison with other results in the literature indicates that the PSO-based algorithm is a viable and effective approach for the job-shop scheduling problem .

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  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China

    Wei-jun Xia & Zhi-ming Wu

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  1. Wei-jun Xia
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  2. Zhi-ming Wu
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Correspondence to Wei-jun Xia.

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Xia, Wj., Wu, Zm. A hybrid particle swarm optimization approach for the job-shop scheduling problem. Int J Adv Manuf Technol 29, 360–366 (2006). https://doi.org/10.1007/s00170-005-2513-4

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  • DOI: https://doi.org/10.1007/s00170-005-2513-4

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