Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty

Abstract

This paper discusses an optimization model for handling the impact of the COVID-19 pandemic based on food supply network through regional food hubs (RFHs) under uncertainty. To this end, uncertainty is assumed in the demand and production data. During the Pandemic COVID-19 period, uncertainty has increased and the food supply chain system has changed. Thus, a new configuration of the food supply network requires analysis. In this paper, the concept of RFH is introduced to connect producers in rural areas and customers in urban areas. This paper determines the location and capacity of RFHs, the food supply network, the sum of maximum food supplies, and minimum logistics cost. This is done via a Multi-Objective Many-to-Many Location-Routing Problem model. Furthermore, since the conditions of the COVID-19 pandemic is uncertain, robust optimization is employed to handle uncertainties. During the current pandemic, red zones are defined to indicate the severity of the pandemic in a region. In this paper, the numerical experiment is considered for three scenarios: when a region is in large-scale social distancing, partial social distancing, or normal conditions. This social distancing situation is based on the defined red zones. The optimal food supply network is obtained for the three scenarios and the best scenario among the three is identified.

Keywords

• Food security
• Regional food hubs
• COVID-19 pandemic
• Multi-objective
• Many-to-many location-routing problem
• Robust optimization