Evaluation of Metaverse integration of freight fluidity measurement alternatives using fuzzy Dombi EDAS model
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Published version
Author(s)
Deveci, Muhammet
Gokasar, Ilgin
Castillo, Oscar
Daim, Tugrul
Type
Journal Article
Abstract
Developments in transportation systems, changes in consumerism trends, and conditions such as COVID-19 have increased both the demand and the load on freight transportation. Since various companies are transporting goods all over the world to evaluate the sustainability, speed, and resiliency of freight transportation systems, data and freight fluidity measurement systems are needed. In this study, an integrated decision-making model is proposed to advantage prioritize the freight fluidity measurement alternatives. The proposed model is composed of two main stages. In the first stage, the Dombi norms based Logarithmic Methodology of Additive Weights (LMAW) is used to find the weights of criteria. In the second phase, an extended Evaluation based on the Distance from Average Solution (EDAS) method with Dombi unction for aggregation is presented to determine the final ranking results of alternatives. Three freight fluidity measurement alternatives are proposed, namely doing nothing, integrating freight activities into Metaverse for measuring fluidity, and forming global governance of freight activities for measuring fluidity through available data. Thirteen criteria, which are grouped under four main aspects namely technology, governance, efficiency, and environmental sustainability, and a case study at which a ground framework is formed for the experts to evaluate the alternatives considering the criteria are used in the multi-criteria decision-making process. The results of the study indicate that integrating freight activities into Metaverse for measuring fluidity is the most advantageous alternative, whereas doing nothing is the least advantageous one.
Date Issued
2022-12
Date Acceptance
2022-11-01
Citation
Computers and Industrial Engineering, 2022, 174, pp.1-14
ISSN
0360-8352
Publisher
Elsevier
Start Page
1
End Page
14
Journal / Book Title
Computers and Industrial Engineering
Volume
174
Copyright Statement
© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S0360835222007616?via%3Dihub
Subjects
Industrial Engineering & Automation
01 Mathematical Sciences
08 Information and Computing Sciences
09 Engineering
Publication Status
Published
Article Number
108773
Date Publish Online
2022-11-08