A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

Sanjoy Kumar Paul; Priyabrata Chowdhury; Ripon Kumar Chakrabortty; Dmitry Ivanov; Karam Sallam

doi:10.1007/s10479-022-04650-2

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

Ann Oper Res. 2022 Apr 11:1-46. doi: 10.1007/s10479-022-04650-2. Online ahead of print.

Authors

Sanjoy Kumar Paul¹, Priyabrata Chowdhury², Ripon Kumar Chakrabortty³, Dmitry Ivanov⁴, Karam Sallam^{5

6}

Affiliations

¹ UTS Business School, University of Technology Sydney, Sydney, Australia.
² School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, Australia.
³ School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.
⁴ Department of Business and Economics, Supply Chain and Operations Management, Berlin School of Economics and Law, Block B, B 3.49, Badensche Str. 50, 10825 Berlin, Germany.
⁵ School of IT and Systems, The University of Canberra, Canberra, Australia.
⁶ The Faculty of Computers and Information, Zagazig University, Zagazig, Egypt.

Abstract

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

Keywords: COVID-19 pandemic; Chance-constrained programming; Recovery planning; Stochastic modelling; Supply chain resilience.