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https://github.com/gabrielcformiga/python-mip


https://github.com/gabrielcformiga/python-mip

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README

          

## From Model to Solution: Mastering Optimization with Python-MIP
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Welcome to "Mastering Optimization with Python-MIP".

First of all, I would like to thank the excellent professor Teobaldo Bulhões — perhaps the best I have had at university, not for theoretical depth, but for his ability to explain a concept simply without losing its essence, something I greatly admired in great minds like Richard Feynman. He was my professor in the Operations Research course at UFPB and introduced me to Python-MIP.

Getting to the point, this repository contains a collection of optimization problem models using Python-MIP and marks my learning journey.

I hope it can serve as a guide for those who wish to learn and apply optimization techniques in their own Mixed-Integer Linear Programming Problems projects.

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## Content
1. [Problems](./src/)
* [Lesson 4 - Product Allocation to Factories](./src/lesson4.ipynb)
* [Lesson 5 - Facility Location](./src/lesson5.ipynb)
* [Lesson 6 - Bin Packing](./src/lesson6.ipynb)
* [Lesson 7 - Traveling Salesman Problem](./src/lesson7.ipynb)
* [Lesson 8 - Steel Company](./src/lesson8.ipynb)
* [Lesson 9 - Agricultural Production](./src/lesson9.ipynb)
* [Lesson 10 - Craftsman's Workshop](./src/lesson10.ipynb)
2. [References](#general-references)
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## General References

* Hillier, F. S., & Lieberman, G. J. (1990). Introduction to Operations Research (7th Edition). McGraw-Hill Education.