Supplier selection and order allocation under uncertainconditions using evolutionary algorithms (case study: Isar general items support company)

Supply Chain Management is one of the new approaches in supply chain management anda competitive advantage for organizations. The purpose of this research is to present a hybridmodel for supplier selection and order allocation under uncertainty in which a two-objectivenonlinear integer programming model is presented to optimize the general supply chain whereall parameters of objective functions and constraints are presented. It is considered uncertain.The method of solving this is that the appropriate weights of the relevant metrics and sub-criteriafor the producers are first obtained by using the organization's criteria through the multi-criteriadecision making method. These weights provide input to the proposed mathematical model. Themathematical model presented is solved as a case study using GAMS software. Then, two multiobjectivemeta-heuristic algorithms, including genetic algorithm and particle optimizationalgorithm with inappropriate sorting, are presented and their results are compared with accuratesoftware for validation. The responses of the meta-algorithms to the GAMS solutions fordifferent problems are less than 2.5%. These results show that the proposed algorithms convergeto the optimal and efficient solution