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A hybrid approach to large-scale job shop scheduling

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Abstract

A decomposition based hybrid optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a simulated annealing approach and then solved using a genetic algorithm. We construct a fuzzy inference system to calculate the jobs’ bottleneck characteristic values which depict the characteristic information in different optimization stages. This information is then utilized to guide the process of subproblem-solving in an immune mechanism in order to promote the optimization efficiency. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems.

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, People’s Republic of China

    Rui Zhang & Cheng Wu

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  1. Rui Zhang
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  2. Cheng Wu
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Correspondence to Rui Zhang.

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Zhang, R., Wu, C. A hybrid approach to large-scale job shop scheduling. Appl Intell 32, 47–59 (2010). https://doi.org/10.1007/s10489-008-0134-y

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  • DOI: https://doi.org/10.1007/s10489-008-0134-y

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