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Journal of Intelligent Manufacturing

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06-04-2023

Bilateral matching for collaborative remanufacturing services based on multi-attribute preferences and mutual interactions

Authors: Xiahui Liu, Liqun Zhang, Qianwang Deng, Meng Li, Chao Jiang

Published in: Journal of Intelligent Manufacturing | Issue 3/2024

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Abstract

The traditional partner selection in reverse logistics focuses on selecting recyclers for remanufacturers, but neglects the selection of remanufactures for recyclers and the mutual matching of the two parties. This is not conducive to forming a sustainable service chain and providing collaborative remanufacturing service solutions for users. In this work, a multi-attribute bilateral matching framework is proposed in accordance with the collaboration between recycling and remanufacturing services. Given the interdependence among service matching attributes and the psychological behavior of decision makers under vague matching surroundings, an integrated methodology is developed to determine service matching satisfaction, which incorporates grey theory, Best–Worst Method, Decision Making Trial and Evaluation Laboratory and an acronym in Portuguese of Interactive and Multi-Criteria Decision Making. On this basis, a collaboration-oriented bilateral matching model concerning recycling and remanufacturing services is constructed with the objectives of maximizing matching satisfaction of remanufacturers and recyclers, as well as achieving service synergy effect. Afterwards, linear weighting method is used to solve the model, and thus the optimal service matching scheme is obtained. The results of the present work provide some references for the optimal allocation of service resources and the transition from traditional remanufacturing into collaborative remanufacturing service.

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Title: Bilateral matching for collaborative remanufacturing services based on multi-attribute preferences and mutual interactions
Authors: Xiahui Liu
Liqun Zhang
Qianwang Deng
Meng Li
Chao Jiang
Publication date: 06-04-2023
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 3/2024
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02111-6

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