Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

Lin Chen, Ting Dong, Jin Peng () and Dan Ralescu
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Lin Chen: School of Management, Wuhan Institute of Technology, Wuhan 430205, China
Ting Dong: School of Management, Wuhan Institute of Technology, Wuhan 430205, China
Jin Peng: Institute of Uncertain Systems, Huanggang Normal University, Huanggang 438000, China
Dan Ralescu: Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 44221, USA

Mathematics, 2023, vol. 11, issue 11, 1-45

Abstract: In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to abstractly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc.

Keywords: uncertainty analysis; optimization modeling; supply chain management; bibliometric analysis; CiteSpace (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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