An efficient multiobjective evolutionary approach for a simultaneous inventory control and facility location problem

Supply chain network system provides an optimal platform for efficient and effective supply chain management (SCM). SCM usually involves multiple and conflicting objectives. A multi-objective location inventory problem (MOLIP) model is initially formulated that includes elements of total cost, volume fill rate and responsiveness level as its objectives and also integrates the effects of facility location, distribution, and inventory issues. In this paper, we presented a hybrid evolutionary algorithm based on the nondominated sorting genetic algorithm (NSGAII) for solving MOLIP. We analyzed a randomly generated set of problem instances of the MOLIP model to understand the model performance and compared this algorithm with one of the well-known multiobjective evolutionary algorithms called SPEA2 to understand the efficiency between two approaches. Computational and comparative results of our NSGAII-based algorithm have presented promise solutions for different sizes of problems and proved to be an innovative and efficient approach for so called difficult-to-solve problems.