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Iterative simulation optimisation approach-based genetic algorithm for lot-sizing problem in make-to-order sector

Author

Listed:
  • Ahmed Ammeri
  • Wafik Hachicha
  • Habib Chabchoub
  • Faouzi Masmoudi

Abstract

This paper describes the development of a process simulation model and integration of the genetic algorithms (GA) with the model as optimisation techniques using a case study of lot sizing problem (LSP) in make-to-order (MTO) supply chain solved by a combined simulation and genetic algorithm (GA) optimisation model. The simulation model is performed using ARENA software. GA model is implemented using visual basic for application (VBA) language because it ensures exchanges between ARENA software and MS Excel. The case study's objective is to determine the optimal solution to determine the fixed lot size for each manufacturing product type that will ensure order mean flow time target. The comparative results with OptQuest software, which is used as a global search method, illustrate the efficiency and effectiveness of the proposed approach.

Suggested Citation

  • Ahmed Ammeri & Wafik Hachicha & Habib Chabchoub & Faouzi Masmoudi, 2014. "Iterative simulation optimisation approach-based genetic algorithm for lot-sizing problem in make-to-order sector," International Journal of Business Performance and Supply Chain Modelling, Inderscience Enterprises Ltd, vol. 6(3/4), pages 376-394.
    • Handle: RePEc:ids:ijbpsc:v:6:y:2014:i:3/4:p:376-394
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