Robust optimization and strategic analysis for agri-food supply chain under pandemic crisis: Case study from an emerging economy.

Pandemic crises like the coronavirus disease 2019 (COVID-19) have severely influenced companies working in the Agri-food industry in different countries. Some companies could overcome this crisis by their elite managers, while many experienced massive financial losses due to a lack of the appropriate strategic planning. On the other hand, governments sought to provide food security to the people during the pandemic crisis, putting extreme pressure on companies operating in this field. Therefore, the aim of this study is to develop a model of the canned food supply chain under uncertain conditions in order to analyze it strategically during the COVID-19 pandemic. The problem uncertainty is addressed using robust optimization, and also the necessity of using a robust optimization approach compared to the nominal approach to the problem is indicated. Finally, to face the COVID-19 pandemic, after determining the strategies for the canned food supply chain, by solving a multi-criteria decision-making (MCDM) problem, the best strategy is specified considering the criteria of the company under study and its equivalent values are presented ​​as optimal values of a mathematical model of canned food supply chain network. The results demonstrated that "expanding the export of canned food to neighboring countries with economic justification" was the best strategy for the company under study during the COVID-19 pandemic. According to the quantitative results, implementing this strategy reduced by 8.03% supply chain costs and increased by 3.65% the human resources employed. Finally, the utilization of available vehicle capacity was 96%, and the utilization of available production throughput was 75.8% when using this strategy.

A novel multi-objective robust fuzzy stochastic programming model for sustainable agri-food supply chain: case study from an emerging economy.

On the one hand, supply chain management of agri-food products under uncertain conditions has a significant impact on food security and, on the other hand, increases the profits of supply chain components. Moreover, considering the sustainability concepts leads to more social and environmental benefits. The present study investigates the canned food supply chain under uncertain conditions and sustainability concepts by considering strategic and operational decisions and different characteristics. The proposed model is a multi-echelon, multi-period, multi-product, multi-objective location-inventory-routing problem (LIRP) in which the vehicle fleet is considered heterogeneously. The objectives of this model are to (1) minimize costs, (2) minimize customer dissatisfaction, (3) maximize production throughput, and (4) maximize job opportunities. In this study, carbon cap and trade mechanism are used to minimize environmental damage. Robust fuzzy stochastic programming (RFSP) is employed to cope and control uncertainties. The multi-objective optimization problem is implemented on a real case and solved using the Torabi and Hassini (TH) method. The results of this study showed that with increasing confidence levels, the severity of the problem increased and the values of the objective functions worsened. Also, using the relative value of stochastic solution (RVSS) criterion demonstrated that the effect of utilizing the RFSP approach on the first and second objective functions was higher than that the nominal approach showed itself. Finally, sensitivity analysis is performed on two parameters: the selling price of products to foreign customers and the cost of purchasing products from farms. The results of this study showed that changing these two parameters had a significant effect on the first and second objective functions.