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A novel scheduling framework: integrating genetic algorithms and discrete event simulation

Author

Listed:
  • Luca Fumagalli
  • Elisa Negri
  • Edoardo Sottoriva
  • Adalberto Polenghi
  • Marco Macchi

Abstract

Most of the research works on methods and techniques for solving the job-shop scheduling problem (JSSP) propose theoretically powerful optimisation algorithms that indeed are practically difficult to apply in real industrial scenarios due to the complexity of these production systems. This paper aims at filling the gap between research and industrial worlds by creating a framework of general applicability for solving the JSSP. Through a literature analysis, the constituent elements of the framework have been identified: an optimisation method that can solve NP-hard JSSP problem in a reasonable time, i.e., the genetic algorithm (GA), and a tool that allows precisely modelling the production system and evaluating the goodness of the schedules, i.e., a simulation model. A case study of a company that bases its business in the manufacturing of aerospace components proved the applicability of the proposed framework.

Suggested Citation

  • Luca Fumagalli & Elisa Negri & Edoardo Sottoriva & Adalberto Polenghi & Marco Macchi, 2018. "A novel scheduling framework: integrating genetic algorithms and discrete event simulation," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 17(4), pages 371-395.
    • Handle: RePEc:ids:ijmdma:v:17:y:2018:i:4:p:371-395
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    Cited by:

    1. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.

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