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)

Minimizing cost of food while satisfying nutritional constraints. i.e optimizing the problem using PuLP through Linear Programming.

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

A Mixed Integer Linear Programming implementation of the Divisor Graph Longest Path problem

This repository summarizes the Flexible Job Work Scheduling Problem with sequence dependent setup in the context of steel manufacturing with special products using python code

Master's | Basic Algorithms & Data structures | Module 10 | Linear Programming & Randomized Algorithms

This repository represents Multi-Factory Network Scheduling MILP optimizer using Python language

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.

Projeto para a etapa de seleção da Residência Tecnológica em Ciência de Dados e Analytics para o setor Automotivo.

Solving Travelling Salesman / Salesperson ( TSP ) - using different algorithms such as Naive ( Brute Force ), Greedy and Integer Programming using Pulp

Solve the shortest path problems using PuLP, a python library used to model linear programming problems

This repository demonstrates how to compute an order driven production schedule using MILP optimizer through Python

Inventory-Optimization-Python-Pulp

Currency optimization using linear programming and Python packages.

Projects for the Operational Reaserch classess @ PUT

Capacitated Facility Location Problem – Optimization project to minimize cost and distance by selecting the best facility locations under capacity constraints.

Max knights on a chessboard problem 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 one-stop solution for beginners and employees who are eager to invest wisely.