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A Genetic Algorithm Approach for Loading Cells with Flow Shop Configuration

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Proceedings of the Eleventh International Conference on Management Science and Engineering Management (ICMSEM 2017)

Part of the book series: Lecture Notes on Multidisciplinary Industrial Engineering ((LNMUINEN))

Abstract

This paper proposes a three-phase methodology for worker allocation and flowshop scheduling in a multistage manufacturing environment. A case study using a shoe manufacturing plant is examined. The proposed methodology consists of genetic algorithm and mathematical models. Phase 1 allocates workers in between operations in two separate stages in the manufacturing process. Phase 1 evaluates how workers are allocated between and within the manufacturing stages. Phase 2 uses worker allocations from phase 1 to perform cell loading based on machine-level-based similarity using genetic algorithms. Four different Genetic Algorithm approaches are proposed and evaluated. Furthermore, the impact of different manpower levels are also studied. Phase 3 schedules products within cells to optimize makespan, total tardiness, or number of tardy jobs.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Ohio University, Athens, OH, 45701, USA

    Patrick Gannon & Gürsel A. Süer

Authors
  1. Patrick Gannon
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  2. Gürsel A. Süer
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Corresponding author

Correspondence to Gürsel A. Süer .

Editor information

Editors and Affiliations

  1. Business and Administration, Sichuan University, Chengdu, China

    Jiuping Xu

  2. Fuzzy Logic Systems Institute, Tokyo University of Science, Tokyo, Tokyo, Japan

    Mitsuo Gen

  3. Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  4. Monash University, Clayton, Victoria, Australia

    Fang Lee Cooke

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Gannon, P., Süer, G.A. (2018). A Genetic Algorithm Approach for Loading Cells with Flow Shop Configuration. In: Xu, J., Gen, M., Hajiyev, A., Cooke, F. (eds) Proceedings of the Eleventh International Conference on Management Science and Engineering Management. ICMSEM 2017. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-59280-0_46

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  • DOI: https://doi.org/10.1007/978-3-319-59280-0_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59279-4

  • Online ISBN: 978-3-319-59280-0

  • eBook Packages: EngineeringEngineering (R0)

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