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

Malin Song (), Sai Yuan (), Hongguang Bo (), Jinbo Song (), Xiongfeng Pan () and Kairui Jin ()
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Malin Song: Anhui University of Finance and Economics
Sai Yuan: Dalian University of Technology
Hongguang Bo: Dalian University of Technology
Jinbo Song: Dalian University of Technology
Xiongfeng Pan: Dalian University of Technology
Kairui Jin: Fudan University

Annals of Operations Research, 2024, vol. 335, issue 3, No 16, 1361 pages

Abstract: 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.

Keywords: Robust optimization model; Anti-epidemic supply chain; Technological innovation; COVID-19 pandemic (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04855-5

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