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Multi-objective Supply Chain Optimization: An Industrial Case Study

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Abstract

Supply chain optimization usually involves multiple objectives. In this paper, supply chains are optimized with a multi-objective optimization approach based on genetic algorithm and simulation model. The supply chains are first modeled as batch deterministic and stochastic Petri nets, and a simulation-based optimization method is developed for inventory policies of the supply chains with a multi-objective optimization approach as its search engine. In this method, the performance of a supply chain is evaluated by simulating its Petri net model, and a Non dominated Sorting Genetic Algorithm (NSGA2) is used to guide the optimization search process towards global optima. An application to a real-life supply chain demonstrates that our approach can obtain inventory policies better than ones currently used in practice in terms of two objectives: inventory cost and service level.

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hal-02556135 , version 1 (27-04-2020)

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Lionel Amodeo, Haoxun Chen, Aboubacar El Hadji. Multi-objective Supply Chain Optimization: An Industrial Case Study. Evoworkshops 2007, Apr 2007, Valencia, Spain. pp.732-741, ⟨10.1007/978-3-540-71805-5_79⟩. ⟨hal-02556135⟩

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