Optimal scheduling of COVID-19 pandemic supply disruptions under uncertainty

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Cao, Wei, Wang, Xifu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: A multiobjective multiperiod mixed-integer programming optimization model; Bi-level Hybrid Genetic Algorithm (BHGA); Bi-level Modified Hybrid Genetic Algorithm (BMHGA); Coronaviruses; COVID-19; COVID-19 pandemic; Emergencies; Genetic algorithms; Integer programming; Job shop scheduling; Mixed integer; Mixed integer linear programming; Multiple objective analysis; Optimization models; Pandemics; Production facilities; Scheduling; Suppliers; supply chain emergency scheduling; Supply chain management; Supply chains; uncertain demand; uncertain supply disruptions; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3218467

With the outbreak of COVID-19 pandemic, the problem of supply chain emergency scheduling has had a great influence on economic benefits of enterprises. The uncertainty of the COVID-19 pandemic and uncertainty of supply chain have made the problem of emergency scheduling more complicated. In this study, a multiobjective multiperiod mixed-integer programming optimization model was developed, in which two conflicting benefit factors, cost and service level were taken as the optimization target of the model. Cost and service level were normalized, and the weighted sum was taken as the objective function, which transformed the problem into a multiperiod nonlinear one. Two algorithms of Bi-level Modified Hybrid Genetic Algorithm (BMHGA) and Bi-level Hybrid Genetic Algorithm (BHGA) were designed to solve the model. Three emergency strategies have been proposed, including enabling alternative suppliers, repairing failure nodes and enabling internal suppliers' flexibility. The parameter of disruption scenario and its solution method were designed. Finally, the practicability of the proposed model and algorithm was demonstrated through application to a case study of an electronics supply chain. The results indicated that the optimal solution of two algorithms was between 2%-5%, and BMHGA could obtain a better optimal solution.