flowshop
by @adimidania
16
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.
About this project
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. Table of content Exhaustive search for FSP Heuristics for FSP Local search based metaheuristics Population based metaheuristics Made with ❤️ by Symbiosis team 🐝. If you have any remarks or inqueries. Please feel free to contact one of the collaborators. Adimi Alaa Dania: jaadimi@esi.dz. Rezkellah Rania FatmaZohra: jfrezkellah@esi.dz. Benazzoug Nourelhouda: jnbenazzoug@esi.dz. Hamzaoui Imane: jihamzaoui@esi.dz. Irmouli Maissa: jmirmouli@esi.dz. Hamitouche Thanina: jthamitouche@esi.dz.
From the project README on GitHub
- Stars
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- Last push
- 6 May 2024
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