PRESENTATION OF A NOVEL, MULTI OBJECTIVE MODEL FOR THE SUPPLIER SELECTION PROBLEM IN A SUPPLY CHAIN, AND ITS SOLUTION USING PARETO-BASED META HEURISTIC ALGORITHMS

Supplier selection is one of the most critical activities of purchasing management in a supply chain, because of the key role of supplier performance in cost, quality, delivery and service towards achieving its objectives. Selecting the right supplier signi cantly reduces purchasing costs and improves corporate competitiveness, which is why many experts believe that supplier selection is the most important activity of a purchasing department. Supplier selection is a multiple-criteria decision-making (MCDM) problem that is aected by several con icting factors. Consequently, a purchasing manager must analyze the trade-o between the several criteria. MCDM techniques support the decision makers (DMS) in evaluating a set of alternatives. In a real situation, for supplier selection problems, the weights of criteriaaredierentanddependonpurchasingstrategies in a supply chain. It is a common practice for suppliers to oer quantity discounts to encourage the buyer towards larger orders. In this case, the buyer must decide what order quantities to assign to each supplier. This is a complicated multiobjective decision-making problem aected by several con icting factors. This paper develops a mixed integer nonlinear programming model to coordinate the system of a single buyer and multiple vendors under an incremental quantity discount policy for the vendors. In this paper, in addition to considering incremental discount strategies, the cost of shortages is also considered. In this model, three goals, including minimization of buyer costs, volume of defective produce and delayed received goods, are considered. Two Pareto-based multi-objective meta-heuristic algorithms, namely; the non-dominated sorting genetic algorithm (NSGA-II) and the non-dominated ranking genetic algorithm (NRGA), are proposed to solve the supplier selection proposed model. Since the solution quality of all meta-heuristic algorithms severely depends on their parameters, the Taghuchi method has been utilized to tune the parameters of the algorithms. Finally, computational results obtained by implementing the algorithmsonseveralproblemsofdierentsizesdemonstrate the performance of the proposed methodologies.