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Population-Based Search Algorithms for Biopharmaceutical Manufacturing Scheduling Problem with Heterogeneous Parallel Mixed Flowshops

Author

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  • Yong Jae Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

  • Hyun Joo Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

  • Byung Soo Kim

    (Department of Industrial and Management Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

Abstract

In this paper, we address biopharmaceutical manufacturing scheduling problems with heterogeneous parallel mixed flowshops. The mixed flowshop consists of three stages, one batch process and two continuous processes. The objective function is to minimize the total tardiness. We formulated a mixed-integer linear programming model for the problem to obtain optimal solutions to small-size problems. We present a genetic algorithm and particle swarm optimization, which are used to find efficient solutions for large-size problems. We show that the particle swarm optimization outperforms the genetic algorithm in large-size problems. We conduct a sensitivity analysis to obtain managerial insights using the particle swarm optimization algorithm.

Suggested Citation

  • Yong Jae Kim & Hyun Joo Kim & Byung Soo Kim, 2025. "Population-Based Search Algorithms for Biopharmaceutical Manufacturing Scheduling Problem with Heterogeneous Parallel Mixed Flowshops," Mathematics, MDPI, vol. 13(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:485-:d:1581314
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