عنوان مقاله [English]
Selecting a appropriate supplier is always a difficult task for most managers. This study is the first to research some of the most short path routing model, selection of suppliers in terms of discussion discount and delay payment on these models, multi-objective considered in with an algorithm, meta-heuristic is solved. In the proposed model, the three goals of minimizing the total cost of the purchase Allowing for fixed costs and delays in the payment of the buyers, minimizing the number of product return and minimizing the path to purchase, According routing of vehicles has been addressed. The mathematical model discounted net present value of the overall amount of money due to delays in payments are considered. The problems of routing and shortest route will be calculated for a set of selected suppliers. All suppliers, their products with discounts and delayed payment offerings. According to the that the purchaser is responsible for collecting the goods purchased and all suppliers have not the same ratio to the non-specified intervals , Therefore, we reduce the distance traveled by the vehicle routing problem in order to find suppliers , and according to the real-world buyers usually have a delay in payment to suppliers , to attract attention due to the increasing willingness of buyers to buy them and use they. In our model, the net present value of the objective function value for purchaser is getting utilized. Because of the large size and complexity of the problem to be NP-Hard, to solve Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA) and Multi objective Particle Swarm Optimization (MOPSO) is proposed. In order to find the shortest path routing model, both Genetic Algorithm (GA) and Simulated Annealing algorithm (SA) to evaluate and select one of them According to the better performance of the algorithm in solved and is used in the multi objective model. Finally, one multi-objective algorithm and another one single objective is recommended to solve this model.