مدل خطی چند هدفه فازی برای انتخاب تامین کنندگان در زنجیره تامین

Within new strategies for purchasing and manufacturing, suppliers play a key role in achieving corporate competition. Hence, selecting the right suppliers is a vital component of these strategies. In practice, vagueness and imprecision of the goals, constraints and parameters in this problem make the decision-making complicated. In spite of the importance of the problem the literature on this subject is relatively scarce. In this paper a fuzzy multiobjective linear model is developed to overcome the vagueness of the information. For the first time in a fuzzy supplier selection problem, an asymmetric fuzzy-decision making technique is applied to enable the decision-maker to assign different weights to various criteria. The model is explained by an illustrative example.

Supplier selection is one of the most critical activities of purchasing management in a supply chain, because of the key role of supplier's performance on cost, quality, delivery and service in achieving the objectives of a supply chain. Supplier selection is a multiple criteria decision-making (MCDM) problem which is affected by several conflicting factors. Consequently, a purchasing manager must analyze the trade off among the several criteria. MCDM techniques support the decision-makers DMs in evaluating a set of alternatives. Depending upon the purchasing situations, criteria have varying mportance and there is a need to weight criteria (Dulmin and Mininno, 2003). In a real situation for a supplier selection problem, many input information are not known precisely. At the time of making decisions, the value of many criteria and constraints are expressed in vague terms such as “very high in quality” or “low in price”. Deterministic models cannot easily take this vagueness into account. In these cases the theory of fuzzy sets is one of the best tools for handling uncertainty. Fuzzy set theories are employed due to the presence of vagueness and imprecision of information in the supplier selection problem. Bellman and Zadeh (1970) suggested a fuzzy programming model for decision-making in fuzzy environments. Zimmermann (1978) first used the Bellman and Zadeh (1970) method to solve fuzzy multiobjective linear programming problems. In his model the fuzzy goals and fuzzy constraints are treated equivalently, which is why the model is called symmetric. It is very common in business activities, such as supplier selection, that the goals importance or weights are different for DMs. Thus, the symmetrical models may not be appropriate for the same multiobjective decision-making problem, because the objectives may not be equally important. In this paper, for the first time, a fuzzy multiobjective model has been developed for the supplier selection problem, in which different weights can be considered for various objectives.

Supplier selection is one of the most important activities of purchasing departments. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services, which affect the outcome in the buyer's company. Supplier selection is a multiple criteria decision-making problem in which the objectives are not equally important. In real cases, many input data are not known precisely for decision-making. For the first time a fuzzy multiobjective model is developed for supplier selection in order to assign different weights to various criteria. This formulation can effectively handle the vagueness and imprecision of input data and the varying importance of criteria in supplier selection problem. The proposed model can help the DM to find out the appropriate ordering from each supplier, and allows purchasing manager(s) to manage supply chain performance on cost, quality, on time delivery, etc. Moreover, through the complete procedure, the fuzzy multiobjective supplier selection problem transforms into a convex (weighted additive) fuzzy programming model and its equivalent crisp single objective LP programming. This transformation reduces the dimension of the system, giving less computational complexity, and makes the application of fuzzy methodology more understandable. Also in this model, the α-cut approach can be utilized to ensure that the achievement level of objective functions should not be less than a minimum level α. Non-linearity in the supplier selection problem, membership function and fuzzy weights are still open for further investigations. In a real situation, the proposed model can be implemented as a vector optimization problem; the basic concept is to use a single utility function to express the preference of DM, in which the values of criteria and constraints are expressed in vague terms and are not equally important.