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Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA)

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  • K. C. Bhosale

    (Savitribai Phule Pune University)

  • P. J. Pawar

    (Savitribai Phule Pune University)

Abstract

In a loading problem of flexible manufacturing system (FMS), part type selection and operations allocation are two critical problems. The total completion time of a product for the selected process plan in the system can be minimum for the loading problem. But, in a real time scheduling system, this process plan may not be optimum because of consideration of waiting time of product and machine. So, the total completion time and thereby the material flow of the selected process plan in the FMS may be high. Due to this problem an integrated approach of part type selection and an operation allocation problem i.e. production planning problem and scheduling problem is considered to optimise material flow in FMS. Loading and scheduling problems are NP-hard in nature. So, to solve complex problems like this, real coded genetic algorithm (RCGA) is used which overcomes some limitations of genetic algorithm. It is observed that, the results of optimisation using RCGA outperforms those obtained by earlier researchers.

Suggested Citation

  • K. C. Bhosale & P. J. Pawar, 2019. "Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA)," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 381-423, June.
    • Handle: RePEc:spr:flsman:v:31:y:2019:i:2:d:10.1007_s10696-018-9310-5
    DOI: 10.1007/s10696-018-9310-5
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    References listed on IDEAS

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    Cited by:

    1. Andrea Maria Zanchettin, 2022. "Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 293-316, June.
    2. Panagiotis D. Paraschos & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2024. "A reinforcement learning/ad-hoc planning and scheduling mechanism for flexible and sustainable manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 714-736, September.
    3. Davide Mezzogori & Giovanni Romagnoli & Francesco Zammori, 2021. "Defining accurate delivery dates in make to order job-shops managed by workload control," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 956-991, December.

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