Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19

Abstract

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

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“…In addition, we found few case studies and quantitative research on disruption and postdisruption periods (Aslam et al, 2023;Modgil et al, 2022;Song et al, 2022). Our analysis of the present literature identifies the need to explore the linkages between the disruption tails, recovery policy, and relocation of DCs (Rozhkov et al, 2022).…”

“…1. Clusters 1 and 2 dealt with the SC dynamics and resilience, which connect the ripple effect with simulation as a solution strategy (El Raoui et al, 2020;Ivanov, 2019;Rozhkov et al, 2022), while clusters 3 and 4 represented the SC portfolio and risk management with optimization as a solution strategy (Ivanov 2021a;Song et al, 2022).…”

“…Choi (2021) coined 'elastic logistics,' which refers to developing or reducing the operational and supply chain capabilities to mitigate disruptions. The operational capabilities included inventory management and facility location, while SC capabilities dealt with DCs and logistics facilities (Kaur et al, 2020;Song et al, 2022). Due to pandemics, operational and supply chain issues have recently received more attention (Kaur et al, 2020;Monostori, 2021).…”

“…COVID-19 has highly impacted the food supply chains due to its time-sensitive supply chain process (Butt, 2021;Cancela et al, 2023;Sentia et al, 2023). Most of the food supply chains all over the world face disruption due to pandemics; for example, in the USA, 5% of the milk output was dumped due to various constraints in the supply chain; similarly, Canada and the United Kingdom have also reported supply chain disruption due to various transportation restrictions (Qingbin et al, 2020;Song et al, 2022). In India, the lockdown resulted in significant financial losses due to disruption and an unplanned pandemic strategy.…”

“…Nevertheless, we examined the feasibility of new routes during the disruption and their probable continuity after returning to normal. The optimal combination of factories and DCs is a challenging aspect of network optimization (Song et al, 2022). Many research studies have explored the relationship between CSC disruption risks and changes in networking behavior.…”