Fuzzy Multiobjective Modeling and Optimization for One-Shot Multiattribute Exchanges With Indivisible Demand

Zhong-Zhong Jiang, Zhi-Ping Fan, W. H. Ip, Xiaohong Chen

Abstract: The modern economy involves a variety of marketplaces, and the Internet has led to the development of a new efficient marketplace-the one-shot multiattribute exchange. It is an important decision problem for a matchmaker (or broker) to achieve the optimal trade matching in one-shot multiattribute exchanges; however, to the best of our knowledge, there has been little work on this issue under fuzzy environments. This paper proposes an optimal matching approach for one-shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations. First, we employ fuzzy set theory to represent the traders' orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom. Second, on the basis of the matching degree, we construct a fuzzy multiobjective programming model for one-shot multiattribute exchanges with indivisible demand. Afterward, the credibility measure is introduced to convert the model into a crisp one. The crisp model belongs to a class of multiobjective nonlinear general assignment problems and has NP-hard complexity. In order to solve the crisp model effectively, we develop a problem-specified metaheuristic algorithm, i.e., multiobjective discrete differential evolution. Finally, we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm.

External IDs:dblp:journals/tfs/JiangFIC16