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Reentrant two-stage multiprocessor flow shop scheduling with due windows

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Abstract

Reentrant flow shop scheduling allows a job to revisit a particular machine several times. The topic has received considerable interest in recent years; with related studies demonstrating that particle swarm algorithm (PSO) is an effective and efficient means of solving scheduling problems. By selecting a wafer testing process with the due window problem as a case study, this study develops a farness particle swarm optimization algorithm (FPSO) to solve reentrant two-stage multiprocessor flow shop scheduling problems in order to minimize earliness and tardiness. Computational results indicate that either small- or large-scale problems are involved in which FPSO outperforms PSO and ant colony optimization with respect to effectiveness and robustness. Importantly, this study demonstrates that FPSO can solve such a complex scheduling problem efficiently.

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Authors and Affiliations

  1. Department of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China

    Rong-Hwa Huang

  2. Graduate School of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China

    Shun-Chi Yu

  3. Graduate Institute of Management, Fu Jen Catholic University, New Taipei City, Taiwan, Republic of China

    Chen-Wei Kuo

Authors
  1. Rong-Hwa Huang
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  2. Shun-Chi Yu
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  3. Chen-Wei Kuo
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Correspondence to Rong-Hwa Huang.

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Huang, RH., Yu, SC. & Kuo, CW. Reentrant two-stage multiprocessor flow shop scheduling with due windows. Int J Adv Manuf Technol 71, 1263–1276 (2014). https://doi.org/10.1007/s00170-013-5534-4

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  • DOI: https://doi.org/10.1007/s00170-013-5534-4

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