Citrus Supply Chain Optimization under Seasonal Demand and Retailer Segmentation

Abstract

Agri-food supply chains are complex to understand and manage. Perishable nature of fresh fruits involves unique problems such as quality and quantity loss under time dependent demand which are difficult to formulate and solve. This research aims to study and optimize multi period mixed-integer linear programming model to minimize fruit waste, carbon emissions, and associated costs. Citrus supply chain is divided into two cases, one being traditional or non-branded supply chain and the other being processed or branded supply chain. Augmented epsilon constraint method with lexicographic optimization is used as solution methodology. Goal programming – weighted sum method is also used to compare methodologies for both branded as well as non-branded supply chains. The models are coded and solved using optimization software, where the results revealed that traditional citrus supply chain encountered less fruit waste as compared to processed citrus hence improving consumption, although it creates a tradeoff between quality and cost. This research includes implications for efficient transportation, enhancing lead time and supply flexibility. The research also includes a binary variable for retailer segmentation that enables selection of market that has maximum capacity to fulfill desired retailer demand.