No abstract

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

Intersection traffic congestion evaluation is essential for effective intelligent transportation system planning, and an objective and precise assessment of traffic congestion is vital to ensure the smooth circulation of traffic. Multiple criteria decision-making is a method for evaluating the degree of traffic congestion. A hybrid multiple criteria decision-making method integrating the fuzzy analytic hierarchy process, techniques for order preference by similarity to an ideal solution, and gray correlation techniques are presented in this work. The proposed method applied fuzzy analytic hierarchy process to determine the weight of the evaluation index; subsequently, gray correlation techniques for order preference by similarity to an ideal solution were integrated to construct the hybrid decision-making method. A case study of traffic congestion at intersections with five evaluation indexes verified the effectiveness of the hybrid method. The evaluation results of the different methods show that the proposed method overcomes the one-sidedness of analytical hierarchy process-techniques for order preference by similarity to an ideal solution and analytical hierarchy process-gray correlation. Thus, the proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the degree of traffic congestion.