High-end equipment: An improved two-sided based S&M matching and a novel Pareto refining method considering consistency. (15th September 2022) - Xiang, Nan; Dou, Yajie; Xia, Boyuan; Yang, Kewei; Tan, Yuejin
Abstract: In order to avoid resources waste and monopoly in high-end equipment manufacturing, the concept of a cloud platform was proposed, in which suppliers and manufacturers can match each other in two-sided. As a result of the increasing information complexity and the fuzziness of human cognition, matching evaluation values may contain some hesitancy and ambiguity. To simulate this situation, we consequently propose an improved two-sided-based S&M matching model and a novel Pareto refining method for a high-end equipment cloud manufacturing platform under a hesitant fuzzy environment. Firstly, the traditional maximum deviation method for calculating attribute weights is improved to adapt to multiple agents in S&M matching. Sequentially, the hesitant fuzzy set (HFS) is applied to describe the fuzziness of decision agents. Simultaneously, we innovatively introduce the score and deviation of HFS to comprehensive satisfaction degree calculating as objective functions. Subsequently, considering the matching consistency, we declare a novel Pareto refining method based on an interval-valued satisfaction matrix to deal with Pareto solutions in the two-sided matching multiple objective model. Finally, an illustrative case is employed to prove the practicability and usability of the two-sided-based S&M matching model in the high-end equipment cloud manufacturing platform. It reveals that this model can not only obtain multiple matching pairs with maximal satisfaction but also can select the most consistent S&M portfolio. Highlights: The maximum deviation method to calculate attribute weights is improved to adapt it to two-sided based S&M matching. The score and deviation functions of the hesitant fuzzy set are introduced into satisfaction calculation. The interval-valued satisfaction matrix is innovatively proposed and applied to Pareto refining according to consistency.