Metaheuristics using for a stochastic supply chain design problem’s resolution -A comparison study

In a competitive, a reliability and a maintainability context, each company find to optimize her supply chain in order to maintain her customers by providing the best quality of products in the best delays and with the lost costs. In this sense, this work addresses a study of a single commodity stochastic distribution network. The distribution network under consideration consists of a set of suppliers serving in a random delivery time a set of retailers through a set of distribution centers (DCs); suppliers are linked to retailers by a single way. So, strategic decisions of suppliers’ selection, DCs location and retailers assignment are integrated in one nonlinear optimization model. Our objective is to find the number of DCs to open and their best location, the best allocation of suppliers to DCs and DCs to retailers. An optimization approach using three different metaheuristics; genetic algorithm simulated annealing and tabu search is used to solve this problem. So, numerical results are presented and compared to show the effectiveness of the each one.