An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Quantitative Models for Supply Chain Management
Quantitative Models for Supply Chain Management
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamical multi-objective optimization using evolutionary algorithm for engineering
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
An innovation approach for achieving cost optimization in supply chain management
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.