A novel robust probabilistic chance constrained programming and strategic analysis for Agri-food closed-loop supply chain under pandemic crisis

Special attention to the Agri-food closed-loop supply chain is more critical during global crises, such as the COVID-19 pandemic. The main reason for this issue is the importance of food security in different communities during various crises. On the other hand, strategic analysis helps Agri-food organizations to be more prepared to face threats and use the opportunities of these crises. Consequently, the combination of supply chain management and strategic analysis can be utilized for better planning in this situation. In the present study, the closed-loop supply chain of canned food in uncertain conditions is investigated. One of the main features of the problem is to use the canned food waste to increase profitability during the COVID-19 pandemic. In addition, the Robust Probabilistic Chance Constrained Programming (RPCCP) approach is presented to face the uncertainty of the problem. One of the main features of the approach is to determine the confidence level of the system proactively to add the concept of robustness to the problem. After using the combined approach and prioritizing strategies for the case study from an emerging economy, "Increased export share of canned food products through virtual marketing and exhibitions" was selected as the superior strategy during the COVID-19 pandemic. Based on the comparison of the results with the organization's current conditions, it was found that the income from the sales of primary products and recycled products increased by 39.8% and 10.79%, respectively. Implementing this strategy indicated that the values of other essential criteria of the closed-loop supply chain also increased.