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Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm

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Abstract

This paper focuses on the scheduling problem of the reconfiguration manufacturing system (RMS) for execution level, where the final objective is to output a production plan. The practical situation in Chinese factory is analyzed, and the characteristics are summarized into the contradiction between flow and job shop production. In order to handle this problem, a new production planning algorithm in virtual cells is proposed for RMS using an improved genetic algorithm. The advantages of this algorithm have three parts: (1) the virtual cell reconfiguration is formed to assist making production plans through providing relationship among task families and machines from cell formation; (2) The operation buffer algorithm is developed for flow style production in cells, which can realize the nonstop processing for flow style jobs; and (3) The multicell sharing method is proposed to schedule job shop jobs in order to fully utilize manufacturing capability among machines in multicells. Based on the above advantages, an improved genetic algorithm is developed to output scheduling plan. At last, the algorithm is tested in different instances with LINGO and the other genetic algorithm, and then the scheduling solution comparison shows the proposed algorithm can get a better optimum result with the same time using the comparison algorithm.

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Authors and Affiliations

  1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Jingsheng Li, Aimin Wang & Chengtong Tang

Authors
  1. Jingsheng Li
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  2. Aimin Wang
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  3. Chengtong Tang
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Corresponding author

Correspondence to Aimin Wang.

Additional information

This project is supported by National Natural Science Foundation of China (51175045).

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Li, J., Wang, A. & Tang, C. Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm. Int J Adv Manuf Technol 74, 47–64 (2014). https://doi.org/10.1007/s00170-014-5987-0

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  • DOI: https://doi.org/10.1007/s00170-014-5987-0

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