A bi-objective robust optimization approach for the management of infectious wastes with demand uncertainty during a pandemic. (10th September 2021)

• Record Type: Journal Article
• Authors: Zhao, Jiahong; Wu, Biaohua; Ke, Ginger Y.
• Is Part Of: Journal of cleaner production. Volume 314(2021)
• Publication Date: 2021-09-10
• Subjects: Infectious waste -- Uncertain waste generation -- Bi-objective robust optimization -- COVID-19 -- Location-routing -- Non-dominated solutions; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.127922
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The current global COVID-19 pandemic attracts public attention to the management of waste generated by health-care activities. Due to the hazardous nature, infectious waste requires the design of a multi-tiered system to provide cost-efficient and eco-friendly services of waste collection, transportation, treatment, and final disposal. However, the impact of uncertainties has not been well studied in the existing literature. Considering the presence of random waste generation during a pandemic, we aim to answer the following questions: 1) where to locate temporary transfer stations and temporary treatment centers; 2) how to plan collection tours among the small generation nodes and temporary transfer stations; 3) how to plan the direct transportation from large generation nodes to treatment centers; 4) how to transport waste from temporary transfer stations to treatment centers, and 5) how to transport wastes from treatment centers to disposal facilities. The relevant cost and associated risk are respectively formulated and assessed using a scenario-based bi-objective robust approach. The complexity of the resulting mathematical model motivated the adaption and comparison of three multi-objective optimization approaches, including the goal programming method, a lexicographic weighted Tchebycheff approach, and an augmented ϵ -constraint solution technique. A case study based on the real situation in Wuhan, China, during the COVID-19 outbreak is conducted to demonstrate the workability of the proposed model and provide managerial insights for infectious waste management. The computational results show that our proposed model can more than double the demand fulfillment rate at an approximately 40% lower cost when facing a distinctively high increment in the amount of infectious waste. Highlights: A robust approach is designed for infectious waste management during a pandemic. System preparedness is enhanced by using facilities compatible with infectious waste. Scenarios for different phases of a pandemic are addressed with probabilities. Three multi-objective methods are adapted and tested for effectiveness and efficiency. A case study based on the COVID-19 outbreak in Wuhan is conducted for insights.