Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions
The study of supply chain (SC) resilience as a research perspective is in an incipient state. Nevertheless, there is a tremendous amount of literature concerning SCs under risk and uncertainty. This paper presents a review of the quantitative models for SC resilience using bibliometric and network analyses. The study identified 3672 articles and provided statistical measurements of science, scientists, and scientific activities. Additionally, the analysis highlights the inter-temporal dimensions of decision making and classifies articles based on their usability in real-world applications. Systematic mapping using co-citation and the PageRank algorithm resulted in seven key research themes, and a microlevel analysis of these themes provides prospective research directions. This involved examining the contributions of individual articles with respect to their scope, value proposition, risk-type consideration, methodology and technique used, and their industry applications. The thematic analysis and extensive future research directions leverage the insights and potential of this review article.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
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Supplemental Notes:
- © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Suryawanshi, Pravin
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0000-0001-5747-9241
- Dutta, Pankaj
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0000-0001-7607-6472
- Publication Date: 2022-1
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; References; Tables;
- Pagination: 102553
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Serial:
- Transportation Research Part E: Logistics and Transportation Review
- Volume: 157
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1366-5545
- Serial URL: http://www.sciencedirect.com/science/journal/13665545
Subject/Index Terms
- TRT Terms: Data models; Decision making; Meta-analysis; Risk; Supply chain management; Uncertainty
- Subject Areas: Data and Information Technology; Freight Transportation; Planning and Forecasting;
Filing Info
- Accession Number: 01840647
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 28 2022 10:37AM