Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach


IŞIK E. E., YILDIZ Ş. A.

EXPERT SYSTEMS WITH APPLICATIONS, vol.229, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 229
  • Publication Date: 2023
  • Doi Number: 10.1016/j.eswa.2023.120510
  • Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Dokuz Eylül University Affiliated: Yes

Abstract

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vac-cines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model in-cludes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncer-tainty in supply, and in the worst case, approximately 30% of the demand cannot be met.