Optimal scheduling of COVID-19 pandemic supply disruptions under uncertainty

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 stu...

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Vydané v:IEEE access Ročník 10; s. 1
Hlavní autori: Cao, Wei, Wang, Xifu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3218467