A fuzzy-TOPSIS approach to enhance emergency logistics supply chain resilience

Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning

Guest editors: Ekaterina Isaeva and Álvaro Rocha

Article type: Research Article

Authors: Ge, Xue^{a; b; *} | Yang, Jiaqi^a | Wang, Haiyan^a | Shao, Wanqing^b

Affiliations: [a] School of Transportation, Wuhan University of Technology, Wuhan, China | [b] School of Economics and Management, Ningbo University of Technology, Ningbo, China

Correspondence: [*] Corresponding author. Xue Ge, E-mail: [email protected].

Abstract: Unconventional emergencies are difficult to be predicted and controlled. Emergency logistics supply chains (SCs) are exposed to great natural risks in operation and prone to encounter chain breakage. Based on a large number of literature analysis and practical research, this paper proposes 8 strategies and develops a Fuzzy-topsis (Technique for Order Preference by Similarity to an Ideal Solution) approach to enhance emergency logistics SC resilience. An empirical analysis is carried out among several Emergency Logistics Experts. The results show that the 3 strategies of “increasing the number of transport links”, “improving information monitoring and warning capabilities”, “improving the accuracy of the plans” and “establishing a green passage” are most effective to enhance the resilience of the emergency logistics SC. Sensitivity analysis shows that the 8 strategies proposed play different roles in the contribution of SC resilience enhancing and can be applied in different situations.

Keywords: Emergency logistics SCs, supply chain risks, supply chain resilience, fuzzy mathematics, TOPSIS

DOI: 10.3233/JIFS-179777

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 6991-6999, 2020