A robust optimization approach to manage uncertainty in local agricultural production in mid-western United States
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Rapid industrialization of agricultural production in developed economies, advancements in information and logistics technologies, emergence of modern food retailers, customer concerns and increased food safety regulations has called for the adoption of supply chain management in the agriculture sector. The agriculture supply chain consists of a series of events in a "farm-to-fork" sequence that includes farming, processing, testing, packaging, warehousing, transportation and distribution. In this thesis, we developed a robust optimization approach to assist the Missouri Coalition for the Environment (MCE) in helping farmers from Missouri and Illinois route products from their farms to a central hub in St. Louis. The aim of this study was to minimize the ton-miles traveled by farmers and MCE vehicles in delivering agricultural products from farms to regional hubs to the central hub. First, a deterministic model was developed that considered the average production at all farms. After looking at historical data about variability of plant and animal products in the Greater Plains region, we developed a robust optimization model accounting for up to 50% variability in these annual production levels at farms. GAMS/CPLEX was used to solve the model under different configurations and identify potential locations for regional hubs.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.