An integrated model for supplier location-selection and order allocation under capacity constraints in an uncertain environment

Facility/supplier location-allocation and supplier selection-order allocation are two of the most important decisions for both designing and operation supply chains. Conventionally, these two issues will be discussed separately. Due to similarity and relationship between these issues, in this paper, we investigate an integrated model for supplier location-selection and order allocation problems in Supply Chain Management (SCM). The objective function is set in such a way that the establishment costs, inventoryrelated costs, and transportation costs as quantitative criteria have been minimized. As regards, the costs are uncertainty; therefore, we have considered them stochastic. This paper develops a bi-objective model for optimization of the mean and variance of costs. Also, the capacities of supplier are limited. This mixed-integer nonlinear program is solved with two meta-heuristic methods: genetic algorithm and simulated annealing. Finally, these two methods are compared in terms of both solution quality and computational time. To obtain a high degree of validity and reliability, the results of GAMS software and meta-heuristic results are compared in small sizes.