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A critical review of best‒worst method applications in the transportation and logistics industry

Mollaoglu Mahmut, Gul Muhammet

LogForum

2025

Vol. 21, no 1

13--33

Background: The Best-worst Method (BWM) has been successfully applied in various fields since it was first proposed in 2015, with numerous extensions developed over time. Its advantages over other pairwise comparison-based multi-criteria decision-making (MCDM) methods-such as requiring fewer pairwise comparisons and providing more consistent evaluations-make it preferable. The primary motivation for this article stems from the fact that no comprehensive review of the method has been published since 2019. The reason for focusing specifically on the “Transportation and Logistics” field is the significant increase in BWM applications within this sector and the large number of BWM studies conducted since the last review article in 2019. Methods: This article provides a bibliometric analysis using VOSviewer visualizations, complemented by a robust interpretation and inference analysis that explores in-depth connections between studies. Specifically, the analysis covers key statistical aspects, including the specific issues to which BWM is applied in the transportation and logistics field, publication trends, methods with which it is integrated, and the concepts (such as fuzzy set, rough set, neutrosophy, stratification etc.) with which it is commonly used. Additionally, the study examines the journals in which these studies are published and the distribution of studies across different countries. Results: The study revealed that several key areas within the transportation and logistics industry, such as occupational health, personnel selection, and the effects of pandemics, remain underexplored. Conclusion: The article highlights emerging research opportunities within the transportation and logistics sector and explores various ideas for the applications of BWM extensions. It also discusses activities, software, books and events related to BWM. The study provides an overview of the current state of BWM applications in transport and logistics and serves as a guide for potential future research.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Zhang Ruogu, Song Shufang

Eksploatacja i Niezawodność

2024

Vol. 26, no. 4

art. no. 190379

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault trees of CPU system and brake signal transmission subsystem are given to verify the effectiveness of the proposed framework.