Hamed Nozari - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Reza Tavakkoli-Moghaddam - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Javid Ghahremani Nahr - Faculty Member of Academic Center for Education, Culture and Research (ACECR), Tabriz, Iran

The worldwide prevalence of coronavirus disease (COVID-۱۹) and the severe problems in the distribution of medical equipment have led to the modeling of multi-depot vehicle routing under uncertainty in the COVID-۱۹ pandemic. The primary purpose of the proposed model is to locate warehouses and production centers and route vehicles for the distribution of medical goods to hospitals. A robust fuzzy method controls uncertain parameters, such as demand, transmission, and distribution costs. The effect of uncertainty using a neutrosophic fuzzy programming method shows that by increasing demand, the volume of medical goods exchanges and the number of vehicles used to distribute goods increase. This leads to an increase in the total cost of the problem and the amount of greenhouse gas (GHG) emissions. The results also show that using more vehicles reduces staff fatigue to distribute medical products and reduces the prevalence of the COVID-۱۹ pandemic. In the most important sensitivity analysis of the problem on the capacity of the vehicle, it was determined that by increasing the capacity of the vehicle, fewer vehicles are used and as a result, the cost and amount of greenhouse gas emissions are reduced. On the other hand, this has led to a decrease in the prevalence of COVID-۱۹ virus.

Multi-depot vehicle routing, Robust fuzzy method, Neutrosophic fuzzy programming, COVID-۱۹ pandemic