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Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions

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  • Suryawanshi, Pravin
  • Dutta, Pankaj

Abstract

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.

Suggested Citation

  • Suryawanshi, Pravin & Dutta, Pankaj, 2022. "Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:transe:v:157:y:2022:i:c:s1366554521003112
    DOI: 10.1016/j.tre.2021.102553
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    Cited by:

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    2. Resende, Carlos Henrique Lopes & Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2023. "Decision-making models for formulating and evaluating supplier development programs: A state-of-the-art review and research paths," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    3. Jiachen Sun & Haiyan Wang & Zhimin Cui, 2023. "Alleviating the Bauxite Maritime Supply Chain Risks through Resilient Strategies: QFD-MCDM with Intuitionistic Fuzzy Decision Approach," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    4. Cao, Yunzhi & Zhu, Xiaoyan & Yan, Houmin, 2022. "Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
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    6. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    7. Mancini, Simona & Gansterer, Margaretha & Triki, Chefi, 2023. "Locker box location planning under uncertainty in demand and capacity availability," Omega, Elsevier, vol. 120(C).
    8. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    9. Pardis Roozkhosh & Alireza Pooya & Renu Agarwal, 2023. "Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach," Operations Management Research, Springer, vol. 16(2), pages 705-725, June.
    10. Yu, Yubing & Xu, Jiawei & Zhang, Justin Z. & Liu, Yulong (David) & Kamal, Muhammad Mustafa & Cao, Yanhong, 2024. "Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagement," International Journal of Production Economics, Elsevier, vol. 270(C).

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