Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic

• Published in: Applied soft computing Vol. 112; p. 107809
• Main Authors: Mosallanezhad, Behzad, Chouhan, Vivek Kumar, Paydar, Mohammad Mahdi, Hajiaghaei-Keshteli, Mostafa
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.11.2021
• Subjects: COVID-19; Disaster relief supply chain; Pandemic; Personal protection equipment; Supply chain design; COVID-19; Personal protection equipment; Pandemic; Supply chain design; Disaster relief supply chain
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107809

The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost. •Develop a multi-objective model for personal protection during the COVID-19.•Optimizing total cost and shortage throughout the network.•Implementation and exploitation of four well-known multi-objective metaheuristics.•Validation of the proposed model through a case study.•Evaluation of the performance metaheuristics using well-known metrics.