چكيده به لاتين
Today, achieving a continuous and mass production line, including in the oil, gas and petrochemical industries, which are being produced 24 hours a day, without interruption, is one of the main concerns for survival in competitive domestic and foreign markets. Therefore, appropriate decisions about suppliers, help to meet the basic needs of industrial equipment in the best possible way. In addition, it is clear that in most real-world problems, supplier selection criteria have interactions with each other, and so traditional aggregation methods, which are usually linear methods, cannot be used to consider this interaction and exert correlations between criteria. In such cases, non-additive aggregation methods are considered.
This study intends to review the literature of non-additive aggregation methods, and then to present a new model based on the best-worst (BWM) technique and Choquet fuzzy integral technique to exert the interaction between criteria, and after that using this model, accomplishes the suppliers evaluation in the Bandar Abbas oil refining company in 2020, as a case study. In the proposed model, it is tried to reduce the effect of detected inconsistency rate on the fuzzy measures by considering the positive or negative interaction between criteria and sub criteria.
To this aim as the first goal, with the help of industry experts, the most effective criteria for identifying suppliers in accordance with the failure mode and effects analysis (FMEA) technique were identified. Then as the second goal, weights of fuzzy measures were exacted by developing a model based on Choquet integral and best-worst technique. Finally, the optimal orders were allocated to suppliers by a multi-objective linear programming (MOLP) model.
The results of this research indicate that in the developed model, a large number of fuzzy measures are reduced and eliminated, which facilitates modeling and solving the problem.
In order to reduce the FMEA risk in selecting suppliers, in addition to the negative and positive interaction between the criteria, the developed model also considers DM preferences and models the problem based on their preferences.
