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A set of Python Jupyter notebooks exploring the Flow Shop Scheduling Problem (FSP) through exhaustive search, heuristics, local search-based metaheuristics, and population-based metaheuristics such as genetic algorithms, focusing on both solution quality and computational efficiency.

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Flowshop scheduling problem

This repository contains multiple Jupyter notebooks focused on optimization algorithms that are designed to solve the flowshop problem. This serves as a project for the Optimization techniques and Artificial Intelligence course at the Higher School of Computer Science ESI.

Note that all the algorithms have been implemented based our coursework, you can find all the lecutres on Tresor ESI via this link.

For reference, all the tests were performed on a computer equipped with an Intel Core i7-6600U processor and 16GB of RAM.

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A set of Python Jupyter notebooks exploring the Flow Shop Scheduling Problem (FSP) through exhaustive search, heuristics, local search-based metaheuristics, and population-based metaheuristics such as genetic algorithms, focusing on both solution quality and computational efficiency.

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