Exact solving scheduling problems accelerated by graph neural networks

@article{Juros2022ExactSS,
  title={Exact solving scheduling problems accelerated by graph neural networks},
  author={Jana Juros and Mario Br{\vc}i{\vc} and Mihael Koncic and Mihael Kovac},
  journal={2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO)},
  year={2022},
  pages={865-870}
}
  • Jana Juros, Mario Brčič, Mihael Kovac
  • Published 23 May 2022
  • Business, Computer Science
  • 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO)
Scheduling is a family of combinatorial problems where we need to find optimal time arrangements for activities. Scheduling problems in applications are usually notoriously hard to solve exactly. Existing exact solving procedures, based on mathematical programming and constraint programming, usually make manually-tuned heuristic choices. These heuristics can be improved by machine learning. In this paper, we apply the graph convolutional neural network from the literature on speeding up general… 
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References

SHOWING 1-10 OF 40 REFERENCES
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
TLDR
A new graph convolutional neural network model is proposed for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs.
Neural Combinatorial Optimization with Reinforcement Learning
TLDR
A framework to tackle combinatorial optimization problems using neural networks and reinforcement learning, and Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
TLDR
This work proposes a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems, and experimentally shows that the framework introduced outperforms the stand-alone RL and CP solutions, while being competitive with industrial solvers.
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning
TLDR
GQSAT is able to reduce the number of iterations required to solve SAT problems by 2-3X, and it generalizes to unsatisfiable SAT instances, as well as to problems with 5X more variables than it was trained on.
Solving Mixed Integer Programs Using Neural Networks
TLDR
This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one.
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
TLDR
This work investigates the use of SPO to solve more realistic discrete optimization problems, and shows for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems.
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