Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, λ-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on λ-fuzzy-measure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify λ-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.
A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system
