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A repository dedicated to the mathematical modeling and solution of optimization problems, featuring practical examples in Stochastic Programming, Linear Programming (LP), and Mixed-Integer Linear Programming (MILP)
A collection of algorithms and methods for solving combinatorial optimization problems, including techniques for TSP, Knapsack, and other NP-hard problems using heuristics, metaheuristics, and exact methods
This project presents a stochastic integer linear programming (ILP) model developed using Python (PuLP) to assist the Bowman family farm in making optimal operational decisions over a one-year planning horizon.
Solving Travelling Salesman / Salesperson ( TSP ) - using different algorithms such as Naive ( Brute Force ), Greedy and Integer Programming using Pulp
A Python web app using Streamlit & PuLP for ILP-based production scheduling. Maximizes profits by assigning products to machines, factoring in batch sizes, setup times, rates, costs, & demand. User-friendly, flexible, & ideal for manufacturing.
A streamlit application to automate the t-shirt distribution process at the Juegos Caribe at the Havana University. The python PuLP library is used to solve the optimization problem.