As part of a university project for a Linear Programming course, I was assigned to represent the Republican Party in a simulated political consulting scenario. The objective was purely analytical: to develop an optimization model that would guide how a Super PAC might allocate a $100 million ad budget across the U.S. to maximize electoral influence. This project was conducted in an academic, nonpartisan context and focused solely on the application of data-driven decision-making and mathematical modeling.


Project Scope & Application of Linear Programming
Linear Programming (LP) is a mathematical technique used to find the optimal outcome in a problem defined by a linear objective function and a set of linear constraints. It is widely used in operations research for resource allocation, logistics, and decision optimization.

In this project, I applied LP concepts to model a real-world budget allocation problem with political and strategic constraints. The challenge involved:
• Formulating the objective function: Maximizing expected campaign impact based on the strategic value of each state.
• Defining decision variables: Each variable represented the dollar amount allocated to a specific state (51 total).
• Modeling constraints: Constraints ensured realistic spending — including total budget limit, per-state minimum/maximum limits, and category-based allocations across four groups: Blue, Red, Swing, and Big 5.

To guide allocation strategy, I used election data from 2020 (voter turnout, electoral votes, historical leaning) and assigned custom weights to states reflecting their strategic importance. A multi-constraint system was designed to capture political nuance while remaining mathematically solvable.

We implemented the model in Excel using OpenSolver, a tool for solving large-scale optimization problems. The solution included:
• A full sensitivity analysis with binding constraints
• Visualizations like pie charts, state-by-state allocation tables, and a U.S. budget heatmap
• Interpretations of shadow prices and constraint tightness to assess robustness

Project Takeaways
This project deepened my understanding of:
• LP theory and practical constraint modeling
• Resource allocation under competing priorities
• Translating complex real-world scenarios into solvable mathematical frameworks
• Using optimization as a tool for strategic planning — beyond just politics

It also sharpened my ability to interpret model outputs, justify decisions using data, and communicate results effectively through visual storytelling and documentation.