An Stochastic Cold Food Supply Chain (CFSC) Design

Abstract

Produce supply chains are a critical part of the food industry, as they are responsible for ensuring that fruits and vegetables are delivered to customers on time, at the right cost, and with the desired quality. Over the past few decades, researchers have been proposing the usage of Hub-and-Spoke networks as a modeling method to optimize large-scale food supply chains. Traditional optimization of supply chains involves identifying and eliminating inefficiencies, reducing lead times, improving inventory management, enhancing supplier relationships, and leveraging technology to improve visibility and control over the entire process. However, the majority of previous models are deterministic and fail to consider the implicit variability of crop yields and the consequence of climate change on the agricultural supply. The increasing amount of CO2 in the atmosphere, along with the shifts in temperatures and weather patterns, may influence harvests. Consequently, forcing distributors and consumers to look up for different suppliers in other areas to compensate for the fluctuations in the supply of produce. In this research, a stochastic Hub-and-spoke network model and an optimization algorithm are proposed to reduce transportation costs by finding an optimal production, distribution, and transportation network while considering climate variability and its impact on crop yield. A case study for an stochastic Cold Food Supply Chain (CFSC) considering several scenarios with different weather conditions is created using climate models and real soil data for the state of California. Strawberry is studied in this work, given that California is one of the major strawberry-producing states in the U.S. The preliminary results of the analysis indicate that weather scenarios showing more precipitation are more likely to increase crop yield, while scenarios with less precipitation yield lower amounts of fresh fruit.