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

Chun Che Huang, Wen Yau Liang, Tzu Liang Tseng, Ping Houa Chen

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)781-795
Number of pages15
JournalJournal of Intelligent Manufacturing
Volume27
Issue number4
DOIs
Publication statusPublished - 2016 Aug 1

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All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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