Masters Theses

Date of Award

8-2025

Degree Type

Thesis

Degree Name

Master of Science

Major

Agricultural Economics

Major Professor

Dr. S. Aaron Smith

Committee Members

Dr. Ricky Chen, Dr. Le Chen

Abstract

Efficient transportation and facility planning are critical components of agricultural supply chains, particularly for high-volume crops such as soybeans and corn. In states like Tennessee, where these commodities play a major economic role, optimizing distribution networks can significantly reduce costs and improve logistical performance. However, factors such as spatial production variability, infrastructure limitations, and market dynamics complicate optimization and efficiency. This paper is divided into three chapters. Chapter 1 develops a linear programming model to minimize soybean transportation costs under multiple market scenarios, including capacity constraints, facility-specific price premiums, and regional cost adjustments. The results highlight how price premiums and regional capacities influence routing decisions, with cost savings achieved through adaptive reallocation of supply. Chapter 2 introduces a Mixed-Integer Linear Programming (MILP) model to identify optimal locations for soybean handling or processing facilities, using production and transportation data. The optimal facility locations cluster around high-output areas in West Tennessee, and sensitivity analysis confirms the robustness of site selection under variable conditions. Chapter 3 applies a similar linear programming approach to corn distribution, analyzing cost-efficient flows under diverse production and demand scenarios across Tennessee. The analysis finds that West Tennessee maintains the lowest transportation costs due to proximity to barge terminals, while facility premiums and industry concentration scenarios can increase costs by over 500% in some regions, particularly in Middle and East Tennessee. These models establish a comprehensive framework for optimizing crop transportation and strategic facility placement, enhancing efficiency across agricultural supply chains in the state of Tennessee? The findings support data-driven decision-making for infrastructure investment and supply chain planning and offer a replicable methodology for improving agricultural logistics in other regions and commodities.

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