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Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling

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Abstract

Multiobjective evolutionary algorithm (MOEA) has attracted much attention in the past decade; however, the application of MOEA to practical problems such as job shop scheduling is seldom considered. In this paper, crowding-measure-based multiobjective evolutionary algorithm (CMOEA) is first designed, which makes use of the crowding measure to adjust the external population and assign different fitness for individuals; then CMOEA is applied to job shop scheduling to minimize makespan and the total tardiness of jobs. Finally, the comparison between CMOEA and SPEA demonstrates that CMOEA performs well in job shop scheduling.

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Authors and Affiliations

  1. Institute of Automation, Shanghai Jiaotong University, 1954 Huashan Road, Shanghai, PR China

    Deming Lei & Zhiming Wu

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  1. Deming Lei
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  2. Zhiming Wu
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Correspondence to Deming Lei.

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Lei, D., Wu, Z. Crowding-measure-based multiobjective evolutionary algorithm for job shop scheduling. Int J Adv Manuf Technol 30, 112–117 (2006). https://doi.org/10.1007/s00170-005-0029-6

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  • DOI: https://doi.org/10.1007/s00170-005-0029-6

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