A Multi-objective Matching Approach for One-Shot Multi-attribute Exchanges Under a Fuzzy Environment

Although many researchers have realized that the trade determination problem in one-shot multi-attribute exchanges is an important decision problem, few studies can be really applied to deal with this issue under a fuzzy environment. This paper proposes a multi-objective matching approach for one-shot multi-attribute exchanges with fuzzy information. First, we use fuzzy set theory to represent the traders' orders with fuzzy information and then give a framework for the proposed multi-objective matching approach. Second, we present a novel calculation method of the matching degree based on the improved fuzzy information axiom. According to the matching degree, we build a fuzzy multi-objective optimization model (FMOM) for the trade determination problem, where two criteria, i. e., profits and satisfactions, are considered simultaneously. Afterward, in order to solve the FMOM, the fuzzy chance constrained programming technique based on credibility measures and the fuzzy goal programming method are introduced to convert it into a crisp single objective one. Finally, we conduct the numerical study and analysis on a practical example to illustrate the application and effectiveness of the proposed multi-objective matching approach.