A scalable real-time solution strategy for supply chain management of fresh produce: A Mexico-to-United States cross border study

Abstract

Inefficiencies in the food supply chain account for up to 60% of food wasted in the United States, significantly inhibiting efforts to tackle food insecurity. In this work, this problem is addressed by developing a supply chain decision-making framework that explicitly considers complex biochemical product quality degradation processes as a function of environmental conditions (e.g., temperature, humidity, atmospheric composition). The resulting optimization problem is solved online in real-time to mitigate demand uncertainty, reducing operating costs, and inventory spoilage. We demonstrate that this approach is equivalent to a data-driven, feedback-based control strategy that relies on manipulating environmental conditions at storage facilities and in transportation equipment. Since large-scale supply chain network instances result in computationally prohibitive optimization problems, a novel and highly efficient heuristic is introduced, that allows for obtaining solutions in practical amounts of time and with negligible degradation in the value of the objective function. The performance of our proposed approach is benchmarked with extensive numerical simulations based on a realistic, large-scale study of the produce supply chain from Mexico to the United States.