Genetic-based Constraint Programming for Resource Constrained Job Scheduling
Author:
Affiliation:
1. RMIT University, Melbourne, Australia
2. Deakin University, Geelong, Australia
3. La Trobe University, Melbourne, Australia
4. Victoria University of Wellington, Wellington, New Zealand
Publisher
ACM
Reference51 articles.
1. An iterated-local-search heuristic for the resource-constrained weighted earliness-tardiness project scheduling problem
2. Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. 2016. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016).
3. I. Bello H. Pham Q. V. Le M. Norouzi and S. Bengio. 2017. Neural Combinatorial Optimization with Reinforcement Learning. https://openreview.net/pdf?id=Bk9mxlSFx
4. Machine learning for combinatorial optimization: A methodological tour d’horizon
5. C. Blum D. Thiruvady A. T. Ernst M. Horn and G. Raidl. 2019. A Biased Random Key Genetic Algorithm with Rollout Evaluations for the Resource Constraint Job Scheduling Problem. In AI 2019: Advances in Artificial Intelligence J. Liu and J. Bailey (Eds.). Springer International Publishing Cham 549--560.
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