The rough set based approach to generic routing problems: case of reverse logistics supplier selection

Chun-Che Huang, Wen-Yau Liang (), Tzu-Liang Tseng and Ping-Houa Chen
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Chun-Che Huang: National Chi Nan University
Wen-Yau Liang: National Changhua University of Education
Tzu-Liang Tseng: The University of Texas at El Paso
Ping-Houa Chen: National Chi Nan University

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 4, No 6, 795 pages

Abstract: Abstract In recent years, Reverse Logistics (RL) has been touted as one of the strategies of improving organization performance and generating a competitive advantage. In RL, the generic routing problem has become a focus since it provides a great flexibility in modeling, e.g., selection of suppliers by using a node as a supplier candidate in a network. To date, complicated networks make decision makers hard to search a desired routine. In addition, the traditional network defines and resolves such a problem only at one soot. The solution cannot be acquired from multiple perspectives like minimal cost, minimal delivery time, maximal reliability, and optimal “3Rs”—reduce, reuse, and recycle. In this study, rough set theory is applied to reduce complexity of the RL data sets and induct decision rules. Through incorporating the decision rules, the generic label correcting algorithm is used to solve generic routing problems by integrating various operators and comparators in the GLC algorithm. Consequently, the desired RL suppliers are selected.

Keywords: Generic reverse logistics; Supplier selection; Rough set approach; Reduce reuse and recycle; Decision rule; Label correcting algorithm (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-014-0913-8

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