ml4vrp
NLNS
| ml4vrp | NLNS | |
|---|---|---|
| 1 | 1 | |
| 12 | 71 | |
| - | - | |
| 3.3 | 1.8 | |
| 2 months ago | almost 4 years ago | |
| Python | Python | |
| - | GNU General Public License v3.0 only |
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ml4vrp
NLNS
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[D] What type of machine learning can be used to solve timetable optimisation problems?
I do not believe anyone has tried ML methods for solving timetable problems yet, so this would be new. My group has come up with several different options for ML+Optimization, but probably our approach "Neural Large Neighborhood Search" will be the most promising here. See our ECAI paper: https://ecai2020.eu/papers/786_paper.pdf, medium post explaining the method: https://dot-bielefeld.medium.com/learning-improvement-heuristics-for-vehicle-routing-problems-with-neural-large-neighborhood-search-6e19252e85f4 and source code: https://github.com/ahottung/NLNS.
What are some alternatives?
or-gym - Environments for OR and RL Research
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
VeRyPy - A python library with implementations of 15 classical heuristics for the capacitated vehicle routing problem.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
awesome-ml4co - Awesome machine learning for combinatorial optimization papers.
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
stable-diffusion-webui - Stable Diffusion web UI
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
cryptosym - SAT-based, MILP, and belief propagation preimage attacks on SHA-256 and other cryptographic hash functions
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.