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Monte Carlo simulation and stochastic algorithms for optimising supply chain management in an uncertain environment

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Abstract

In this article, we consider a supply chain with stochastic demands and delivery times. We try to find optimal parameters which will allow us to reach performances related to the percentage of customers satisfied. For this purpose, we use Monte Carlo simulation and two meta-heuristics; taboo and kangaroo methods. Furthermore, short term and long term strategy are considered. This method allows us to optimize our system considering stochastic parameters and prediction errors. Thus, we use statistical tests to compare results given by Monte Carlo simulation. Numerical results are given in a special case. The same approach can be used to more complex problems dealing with uncertain environment.
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hal-02353055 , version 1 (07-11-2019)

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Olfa Jellouli, Eric Chatelet. Monte Carlo simulation and stochastic algorithms for optimising supply chain management in an uncertain environment. IEEE International Conference on Systems, Man & Cybernetics, Oct 2001, Tucson, United States. pp.1840-1844, ⟨10.1109/ICSMC.2001.973600⟩. ⟨hal-02353055⟩
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