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. • Perform a review of quantitative models on supply chain risk and resilience. • Evaluate 3672 articles and perform bibliometric and network analysis. • Present research clusters based on Co-citations and PageRank algorithm. • Explore various modeling approaches, risk analysis, and solution techniques. • Analyze seven key research themes and identify prospective research directions.