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Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

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Abstract

This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problem with re-entrant jobs. The objective of this research is to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs reenter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

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Authors and Affiliations

  1. Division of Systems and Engineering Management, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Carman K. M. Lee, Danping Lin & Zhang Wu

  2. Operations and Information Management Group, Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK

    William Ho

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  1. Carman K. M. Lee
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  2. Danping Lin
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  3. William Ho
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  4. Zhang Wu
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Correspondence to Danping Lin.

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Lee, C.K.M., Lin, D., Ho, W. et al. Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs. Int J Adv Manuf Technol 56, 1105–1113 (2011). https://doi.org/10.1007/s00170-011-3251-4

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