Sustainable Cold Supply Chain Design for Livestock and Perishable Products Using Data-Driven Robust Optimization

Food products are a critical part of everyday life. These products have different types, like perishable, livestock, etc. To increase the efficiency of the food supply chain, designing a comprehensive mathematical model that considers all crucial aspects of the chain is necessary. Therefore, this study developed a novel model to integrate livestock and perishable products in a three-echelon network. The model has three objective functions maximize the total profit of breeding livestock and perishable products, minimize the total storage cost of the perishable products in the cold chain and minimize the health risk of the customers. In dealing with uncertainty, a data-driven robust optimization method has been used. Therefore, this paper used machine learning to construct the uncertainty sets from many historical data. The result shows that this methodology has a significant influence on total profits(about 32%) and environmental criteria(about 12%) in a real-world case study