Providing a Combined Model of Supplier Selection and Order Allocation in Random Uncertainty Conditions and Solution with the Robust Fuzzy Optimization Approach (Case Study: Isar General Commodities Company)

The purpose of this research is to present a two-objective nonlinear integer programming model for optimization of a general commodities supply chain, minimizing the purchase costs and delivery delays and maximizing the resilience with uncertain constraints and parameters of the objective functions. To solve the model, the weights of relevant metrics and sub-criteria for producers are obtained through multi-criteria decision making. These metrics are used as the input data to the proposed mathematical model. Since this is classified as a hybrid optimization problem in the NP-hard problem family, NSGA-II and MOPSO multi-objective evolutionary algorithms are used to solve the proposed model. Comparing the results of the algorithm is done with the help of comparative indices. In this study, the probability constraint programming is used for the fuzzy to deterministic conversion of models. This method fits the LAM and UAM estimation models appropriately to the pessimistic-optimistic changes due to the differences in the decision makers' attitudes. Results of comparing these algorithms indicate that the genetic algorithm performs better in the optimistic view, whilst the MOPSO algorithm performs better in the pessimistic case. The results of NSGA-II and MOPSO algorithm for the designed sample problem show that NSGA-II algorithm performs better than MOPSO algorithm in different criteria. For instance, better performance regarding the solution time and the criterion of the distance from the ideal point, is observed in all problems.