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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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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