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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chun-Che Huang & Wen-Yau Liang & Tzu-Liang Tseng & Ping-Houa Chen, 2016. "The rough set based approach to generic routing problems: case of reverse logistics supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 781-795, August.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:4:d:10.1007_s10845-014-0913-8
    DOI: 10.1007/s10845-014-0913-8
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    References listed on IDEAS

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    1. Srivastava, Samir K., 2008. "Network design for reverse logistics," Omega, Elsevier, vol. 36(4), pages 535-548, August.
    2. Huang, Chun Che & Liang, Wen Yau & Lin, Shian Hua, 2009. "An agile approach for supply chain modeling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(3), pages 380-397, May.
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    Cited by:

    1. Olga Lingaitienė & Aurelija Burinskienė & Vida Davidavičienė, 2022. "Case Study of Municipal Waste and Its Reliance on Reverse Logistics in European Countries," Sustainability, MDPI, vol. 14(3), pages 1-24, February.
    2. Ilkka Ritola & Harold Krikke & Marjolein C.J. Caniëls, 2020. "Learning from Returned Products in a Closed Loop Supply Chain: A Systematic Literature Review," Logistics, MDPI, vol. 4(2), pages 1-13, April.
    3. Piotr Sawicki & Hanna Sawicka & Marek Karkula & Krzysztof Zajda, 2025. "Combined Rough Sets and Rule-Based Expert System to Support Environmentally Oriented Sandwich Pallet Loading Problem," Energies, MDPI, vol. 18(2), pages 1-48, January.
    4. Josip Marić & Marco Opazo-Basáez, 2019. "Green Servitization for Flexible and Sustainable Supply Chain Operations: A Review of Reverse Logistics Services in Manufacturing," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 65-80, December.

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