Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61701
Title: Fuzzy multiobjective modeling and optimization for one-shot multiattribute exchanges with indivisible demand
Authors: Jiang, ZZ
Fan, ZP
Ip, WH 
Chen, X
Keywords: Differential evolution
E-commerce
Fuzzy information
Multi-Attribute exchanges
Multi-objective optimization
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2016, v. 24, no. 3, 7239603, p. 708-723 How to cite?
Journal: IEEE transactions on fuzzy systems 
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.
URI: http://hdl.handle.net/10397/61701
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2015.2476516
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