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.

Coronavirus disease (COVID-19) has spread for over a year and affected many aspects, including the food supply chain. One of the ways COVID-19 has impacted the food supply chain is the food production capacity reduction. It is necessary to develop the optimum food supply chain strategy by determining the optimum food hub location and food network to maintain food security which robust against disruptions and uncertainties. In this study, Robust Optimization (RO) is applied to handle the uncertainties. Nevertheless, the actual uncertain data might be hard to be collected or even unavailable at the moment. Therefore, an innovative framework is proposed to integrate RO with Agent-Based Modelling (ABM). ABM is used to simulate the upstream actor of the food supply chain and predict the uncertain food production capacity, which RO later handles. Particularly, this study focused on rice supply chain. The result shows that the framework is able to handle the uncertain rice supply chain problem, in which the actual uncertain data might be unavailable, and give the robust optimum food hub location and food network. The food hub location and food network are obtained by solving the Robust Counterpart (RC) model with respect to the uncertainty set obtained from the ABM simulation result.

Food security is the state of having reliable access to a sufficient quantity of affordable, safe, and nutritious food for all people. It is a critical point to be achieved yet has many aspects to be considered, which include food availability, stability, access, and utilization. Each aspect has its own challenge, which makes food security a complex goal to achieve. Therefore, this paper aims to demonstrate how the Local Food Network (LFN) might be able to address the challenges of food security and eventually achieve it. Moreover, this paper also takes the standpoint of sustainability aspects to ensure food security can be achieved sustainably and responsibly. A case study in Indonesia is given in this paper to provide a concrete explanation of the topic. Rice commodity is used in this study as the staple food of Indonesia. To achieve the goal of this paper, a Multi-objective Linear Programming (MOLP) model, which reflects the LFN, is developed. Furthermore, sustainability’s social, economic, and environmental aspects are directly incorporated into the model. Through several measures obtained from the model results, this paper indicates that food security can be achieved sustainably through the concept of LFN.