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.