About Pyomo

Pyomo is a Python-based open-source optimization modeling language. It allows you to express complex optimization problems in a simple and intuitive manner. With Pyomo, you can formulate, solve, and analyze optimization models efficiently.

What You’ll Find in This Repository

In this repository, you’ll discover a collection of Jupyter notebooks, each tackling a specific optimization problem. These notebooks provide a step-by-step guide, including:

• Problem Description: A clear explanation of the optimization problem at hand.
• Model Formulation: How the problem is mathematically formulated as an optimization model.
• Pyomo Implementation: Code snippets demonstrating how to implement the model in Pyomo.
• Solution: How to solve the problem using optimization solvers like Gurobi or CBC.
• Visualization and Analysis: Visual representations of results and in-depth analysis of the solutions.

LinkedIn Newsletter

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Telegram Channel —> https://t.me/PyomoChannel

Udemy training course

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Solved Projects

Solved Projects
Pyomo on Neos Server	Vertex coloring
Maximum number of points with min specified Distance	Easter photo arrangement
Biggest circle with equal blue and green points	Student - Prof assignment
Christmas gift- happiness maximization	Knight’s Tour
Island Detection in Graphs	Multi-curve Regression
7Seen Modeling in Pyomo	Number-link
April fool’s	Conquering all metro stations
Critical Node detection	Hidato puzzle
Edge coloring	Hashi puzzle
Empty bucket problem	Bonairo puzzle
Christmas gift	Shikaku puzzle
Graph island detection	Magic Square puzzle
Longest common sequence	Monochrome Rectangle puzzle
Bilevel Optimization in Pyomo	TSP AddCircuit
15Puzzle IBM Challenge	Secret Santa
Family Riddle Challenge	TSP with precedence
Prime number IBM Challenge	Movie Shooring Sequence
Master mind game CP & Pyomo	Optimal order pickup from inventory (CP)
SUguru Puzzle CP	Shape Palacement CP
CVRP CP	Stable Marriage CP
Largest_box_CP

Contributing Guidelines

We encourage contributions to make this repository even more valuable. If you’d like to contribute, please follow these guidelines:

Fork the repository and create a new branch for your contribution.
Ensure your code is clean, readable, and well-documented.
Test your changes to guarantee their correctness and effectiveness.
Submit a pull request, providing clear descriptions of your modifications and any relevant context.
Let’s work together to build a vibrant hub of optimization knowledge and expertise!