Nowadays, fierce competition in global markets has forced companies to improve the design and management of supply chains, and provide competitive advantages. Decision integrity is one of the main factors which highly lead to a considerable reduction of supply chain costs, and higher costumer’s satisfaction. Distribution network design is based on three major problems: location allocation, vehicle routing and inventory control. Since the effective role of reducing distribution costs in the survival of the supply chain is clear to all, in this paper, these three problems will be incorporated into an integrated model under demand uncertainty. This approach leads to the significant reduction of distribution costs, higher customer satisfaction, and also providing an efficient supply chain. Also in this study, in addition to minimizing the total cost including fixed cost of establishing depots, transportation costs and inventory costs, the customers’ satisfaction will increase by reducing their waiting time. So, a bi-objective mixed integer non-linear model is presented by using chance constrained programming, where customer demands are assumed to have a normal distribution. Then, to solve the model, a hybrid algorithm based on simulated annealing and genetic algorithm is proposed, and is evaluated on a set of instances. The computational results illustrate the algorithm efficiency to solve a wide range of problems with different sizes.
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