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A multi-product lot size in make-to-order supply chain using discrete event simulation and response surface methodology

Author

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

Abstract

This paper develops a simulation optimisation approach for solving the Lot-Sizing Problem (LSP) in Make-to-Order (MTO) supply chain. For this purpose, a discrete event simulation model was firstly implemented as a tool in estimating Order Mean Flow Time (OMFT) performance. Secondly, a multiple-objective optimisation was achieved by applying Response Surface Methodology (RSM). A comprehensive case study is detailed which involves a multi-product, multi-stage, multi-location production planning with capacity-constrained and stochastic parameters such as lot arrivals order, transit time, set-up time, processing time, etc. The objective of the proposed approach is to determine the fixed optimal lot size for each manufacturing product type that will ensure OMFT target value for each finished product type. The study results illustrate that the LSP in MTO sector is viable and provide a prototype for further research on simulation optimisation approaches.

Suggested Citation

  • Wafik Hachicha & Ahmed Ammeri & Faouzi Masmoudi & Habib Chabchoub, 2010. "A multi-product lot size in make-to-order supply chain using discrete event simulation and response surface methodology," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 2(3/4), pages 246-266.
    • Handle: RePEc:ids:injsem:v:2:y:2010:i:3/4:p:246-266
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    Citations

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    Cited by:

    1. Chia-Nan Wang & Thanh-Tuan Dang & Ngoc-Ai-Thy Nguyen, 2020. "A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology," Mathematics, MDPI, vol. 8(8), pages 1-23, July.
    2. Hatem Elleuch & Wafik Hachicha & Habib Chabchoub, 2014. "A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study," Journal of Risk Research, Taylor & Francis Journals, vol. 17(5), pages 641-663, May.

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