A Mixed Integer Linear Programming Approach to Optimize Resource Distribution

This project implements an Optimization Model using Mixed Integer Linear Programming (MILP) to solve a resource allocation or scheduling problem. The notebook likely defines decision variables, constraints, and an objective function that aims to minimize or maximize a specific goal, such as cost, time, or efficiency. Using optimization libraries (commonly PuLP, Pyomo, or Gurobi), the model mathematically formulates relationships between inputs and decisions, then computes the optimal solution that satisfies all constraints. In essence, the code automates the process of finding the best possible combination of decisions under given limitations — a core task in operations research, logistics, and data-driven decision-making.