Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information

Abstract

Due to the increasing complexity in socioeconomic environments and the fuzziness in human cognition, the cognitive information over alternatives provided by a decision organization consisting of several experts is usually uncertain and hesitant. Consequently, in order to solve the fuzziness and uncertainty of preference information in the matching process of complex product manufacturing tasks on the cloud manufacturing platform, a novel two-sided matching model based on dual hesitant fuzzy preference information is proposed. Firstly, the two-sided matching problem and the dual hesitant fuzzy set (DHFS) are described. Then, the dual hesitant fuzzy preference information evaluation matrix is constructed and normalized according to the preference information provided by agents on both sides. Sequentially, the dual hesitant fuzzy preference information evaluation matrix is transformed into the satisfaction degree matrix by the projection technology. Simultaneously, considering the stable matching constraint, a multi-objective two-sided matching optimization model which maximizes the satisfaction degree and minimizes the difference degree of two-sided agents is established. Further, the matching relative competition degree is used to convert the multi-objective optimization model into a single-objective optimization model, and the matching algorithm is designed for solving the model. Moreover, an illustrative example is employed to demonstrate the practicality and feasibility of the developed model. Subsequently, the sensitivity analysis is performed to validate the stability of the proposed matching results, and the comparative analysis is carried out to illustrate the reliability of the proposed matching algorithm and the merits of the developed model. It reveals that the proposed model can not only obtain stable matching results but also give more choices in matching methods.