Papers
Topics
Authors
Recent
Search
2000 character limit reached

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem

Published 27 Feb 2024 in cs.LG and cs.AI | (2402.17606v3)

Abstract: Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based on the message-passing mechanism to calculate the forward and backward topological sorts of the DG, which are the features for characterizing the topological structures and exploited by our model. In addition, we theoretically and experimentally show that TBGAT has linear computational complexity to the number of jobs and machines, respectively, strengthening our method's practical value. Besides, extensive experiments on five synthetic datasets and seven classic benchmarks show that TBGAT achieves new SOTA results by outperforming a wide range of neural methods by a large margin. All the code and data are publicly available online at https://github.com/zcaicaros/TBGAT.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. The shifting bottleneck procedure for job shop scheduling. Management Science, 34(3):391–401, 1988.
  2. A computational study of the job-shop scheduling problem. ORSA Journal on Computing, 3(2):149–156, 1991.
  3. Egon Balas. Machine sequencing via disjunctive graphs: an implicit enumeration algorithm. Operations research, 17(6):941–957, 1969.
  4. The disjunctive graph machine representation of the job shop scheduling problem. European Journal of Operational Research, 127(2):317–331, 2000.
  5. Openai gym. arXiv preprint arXiv:1606.01540, 2016.
  6. A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job shop scheduling problem. IEEE Transactions on Industrial Informatics, 2022.
  7. Industrial size job shop scheduling tackled by present day cp solvers. In International Conference on Principles and Practice of Constraint Programming, pages 144–160. Springer, 2019.
  8. Machine learning and data mining in manufacturing. Expert Systems with Applications, 166:114060, 2021.
  9. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428, 2019.
  10. Henry Fisher. Probabilistic learning combinations of local job-shop scheduling rules. Industrial Scheduling, pages 225–251, 1963.
  11. The complexity of flowshop and jobshop scheduling. Mathematics of operations research, 1(2):117–129, 1976.
  12. The vehicle routing problem: latest advances and new challenges, volume 43. Springer, 2008.
  13. Efficient active search for combinatorial optimization problems. In International Conference on Learning Representations, 2021.
  14. Learning to generalize dispatching rules on the job shop scheduling. arXiv preprint arXiv:2206.04423, 2022.
  15. Deterministic job-shop scheduling: Past, present and future. European journal of operational research, 113(2):390–434, 1999.
  16. Learning the travelling salesperson problem requires rethinking generalization. Constraints, pages 1–29, 2022.
  17. Dieter Jungnickel and D Jungnickel. Graphs, networks and algorithms, volume 3. Springer, 2005.
  18. Attention, learn to solve routing problems! In International Conference on Learning Representations, 2018.
  19. Matrix encoding networks for neural combinatorial optimization. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 5138–5149. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper/2021/file/29539ed932d32f1c56324cded92c07c2-Paper.pdf.
  20. Stephen Lawrence. Resouce constrained project scheduling: An experimental investigation of heuristic scheduling techniques (supplement). Graduate School of Industrial Administration, Carnegie-Mellon University, 1984.
  21. A fast taboo search algorithm for the job shop problem. Management science, 42(6):797–813, 1996.
  22. Schedulenet: Learn to solve multi-agent scheduling problems with reinforcement learning. arXiv preprint arXiv:2106.03051, 2021a.
  23. Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research, 59(11):3360–3377, 2021b.
  24. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
  25. Or-tools. URL https://developers.google.com/optimization/.
  26. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
  27. New search spaces for sequencing problems with application to job shop scheduling. Management Science, 38(10):1495–1509, 1992.
  28. Eric Taillard. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2):278–285, 1993.
  29. A reinforcement learning environment for job-shop scheduling. arXiv preprint arXiv:2104.03760, 2021.
  30. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  31. Graph attention networks. In International Conference on Learning Representations, 2018.
  32. Electronic design automation: synthesis, verification, and test. Morgan Kaufmann, 2009.
  33. Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3):229–256, 1992.
  34. Multi-decoder attention model with embedding glimpse for solving vehicle routing problems. In Proceedings of 35th AAAI Conference on Artificial Intelligence, pages 12042–12049, 2021.
  35. How powerful are graph neural networks? In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=ryGs6iA5Km.
  36. A genetic algorithm applicable to large-scale job-shop problems. In PPSN, volume 2, pages 281–290, 1992.
  37. Learning to dispatch for job shop scheduling via deep reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 1621–1632. Curran Associates, Inc., 2020. URL https://proceedings.neurips.cc/paper/2020/file/11958dfee29b6709f48a9ba0387a2431-Paper.pdf.
  38. Deep reinforcement learning guided improvement heuristic for job shop scheduling. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=jsWCmrsHHs.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up