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A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management

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  • Sheu, Jiuh-Biing

Abstract

This paper presents a hybrid neuro-fuzzy methodology to identify appropriate global logistics (GL) operational modes used for global supply chain management. The proposed methodological framework includes three main developmental phases: (1) establishment of a GL strategic hierarchy, (2) formulation of GL-mode identification rules, and (3) development of a GL-mode choice model. By integrating advanced multi-criteria decision-making (MCDM) techniques including fuzzy analytical hierarchy process (Fuzzy-AHP), Fuzzy-MCDM, and the technique for order preference by similarity to an ideal solution (TOPSIS), six types of global logistics and operational modes coupled with corresponding fuzzy-based multi-criteria decision-making rules are specified in the second phase. Using the specified fuzzy decision-making rules as the input database, an adaptive neuro-fuzzy inference system (ANFIS) is then developed in the third phase to identify proper GL modes for the implementation of global supply chain management. A numerical study with a questionnaire survey database aimed at the information technology (IT) industries of Taiwan is conducted to illustrate the applicability of the proposed method.

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  • Sheu, Jiuh-Biing, 2008. "A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management," European Journal of Operational Research, Elsevier, vol. 189(3), pages 971-986, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:971-986
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    Cited by:

    1. Feng, Cheng-Min & Wu, Pei-Ju & Chia, Kai-Chieh, 2010. "A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: A case study," European Journal of Operational Research, Elsevier, vol. 200(1), pages 63-73, January.
    2. Bouzon, Marina & Govindan, Kannan & Rodriguez, Carlos M.Taboada & Campos, Lucila M.S., 2016. "Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP," Resources, Conservation & Recycling, Elsevier, vol. 108(C), pages 182-197.
    3. Sheu, Jiuh Biing & Kundu, Tanmoy, 2018. "Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 117(C), pages 5-22.
    4. Barros, Carlos Pestana & Wanke, Peter, 2015. "An analysis of African airlines efficiency with two-stage TOPSIS and neural networks," Journal of Air Transport Management, Elsevier, vol. 44, pages 90-102.
    5. Kozarević, Safet & Puška, Adis, 2018. "Use of fuzzy logic for measuring practices and performances of supply chain," Operations Research Perspectives, Elsevier, vol. 5(C), pages 150-160.
    6. Jiang, Yonglei & Sheu, Jiuh-Biing & Peng, Zixuan & Yu, Bin, 2018. "Hinterland patterns of China Railway (CR) express in China under the Belt and Road Initiative: A preliminary analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 189-201.
    7. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    8. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.
    9. Peter Wanke & Carlos Barros & Nkanga Pedro João Macanda, 2016. "Predicting Efficiency in Angolan Banks: A Two-Stage TOPSIS and Neural Networks Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 461-483, September.

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