the use of metaheuristics as the resolution for stochastic supply chain design problem: a comparison study

In a competitive and maintainability context, each company looks for optimizing its supply chain in order to satisfy its customers by providing the best quality products in the best delays and with the lost costs. in this sense, we are interested in a single- commodity stochastic supply chain design problem. our supply chain is composed of suppliers and retailers. the objective is to find the best location for distribution centres (dcs) and to serve retailers from suppliers through dcs in a random supply lead time. we presented a non-linear optimization model that integrates the selection of suppliers, the location of dcs, and the retailers’ allocation decisions with an oriented cost function to minimize. note that the determination of exact solutions to this problem is a np-hard problem. accordingly, we proposed an optimization approach using three different metaheuristics: genetic algorithm, simulated annealing, and taboo search to solve this problem and find the best supply chain structure (location of dcs, allocation of suppliers to dcs and dcs to retailers). computational results are presented and compared to evaluate the efficiency of the proposed approaches.