A new heuristic method for distribution networks considering service level constraint and coverage radius

This paper presents a new mathematical model for designing distribution networks in a supply chain system considering service level constraint optimizing strategic decisions (location), tactical decisions (inventory), and assigning decisions. In real-world cases, demand, traveling time or any parameters in classical models may change over the period of time. So, considering uncertainty yields more flexibility for the results and the proposed model. In our study, environmental uncertainty is described by discrete scenarios. In this model, we have service level constraint in order to prevent inventory lost in distribution centers (DCs). Also, we assume that customer’s demand is stochastic with Poisson distribution function and DCs have coverage radius constraints thus any DC cannot service all the customers. In this model, location of DCs is selected and optimized and the best flow of products from supplier to DCs also from DCs to customers is determined. In this way, the customers’ demand should be satisfied at least service level. To solve this nonlinear integer programming model we first present a new and robust solution based on a genetic search framework and then based on genetic algorithm results and some optimizer rules we propose a new heuristic method. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithms.