Paper Info
Title
Multi-objective Supply Chain Optimization: An Industrial Case Study
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.
Year
DOI
Venue
2007
10.1007/978-3-540-71805-5_79
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Keywords
Field
DocType
industrial case study,supply chain optimization,genetic algorithm,real-life supply chain,multi-objective optimization approach,supply chain,multi-objective supply chain optimization,inventory cost,simulation-based optimization method,search engine,optimization search process,inventory policy,simulation,supply chain management,petri net,multi objective optimization
Mathematical optimization,Service level,Petri net,Systems engineering,Supply chain optimization,Multi-objective optimization,Stochastic Petri net,Supply chain management,Supply chain,Engineering,Genetic algorithm
Conference
Volume
ISSN
Citations 
4448
0302-9743
13
PageRank 
References 
Authors
0.81
7
3
Name
Order
Citations
PageRank
Lionel Amodeo132526.83
Haoxun Chen277360.23
aboubacar el hadji3130.81