Optimizing Locations and Scales of Distribution Centers Under Uncertainty

In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers' locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.