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Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning (2210.17178v2)

Published 31 Oct 2022 in cs.LG and cs.NE

Abstract: The permutation flow shop scheduling (PFSS), aiming at finding the optimal permutation of jobs, is widely used in manufacturing systems. When solving large-scale PFSS problems, traditional optimization algorithms such as heuristics could hardly meet the demands of both solution accuracy and computational efficiency, thus learning-based methods have recently garnered more attention. Some work attempts to solve the problems by reinforcement learning methods, which suffer from slow convergence issues during training and are still not accurate enough regarding the solutions. To that end, we propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Moreover, in order to extract better feature representations of input jobs, we incorporate the graph structure as the encoder. The extensive experiments reveal that our proposed model obtains significant promotion and presents excellent generalizability in large-scale problems with up to 1000 jobs. Compared to the state-of-the-art reinforcement learning method, our model's network parameters are reduced to only 37\% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8\% to 1.3\% on average. The code is available at: \url{https://github.com/longkangli/PFSS-IL}.

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References (40)
  1. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, 1.
  2. Automatic algorithm design for hybrid flowshop scheduling problems. European Journal of Operational Research, 282(3): 835–845.
  3. Learning to learn by gradient descent by gradient descent. In Advances in neural information processing systems, 3981–3989.
  4. Layer normalization. Advances in NIPS Deep Learning Symposium.
  5. Neural combinatorial optimization with reinforcement learning. International Conference on Learning Representations.
  6. Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research.
  7. Bixby, B. 2007. The gurobi optimizer. Transp. Re-search Part B, Gurobi Corporation, 41(2): 159–178.
  8. An experimental study of neural networks for variable graphs. International Conference on Learning Representations Workshop.
  9. Learning combinatorial optimization algorithms over graphs. In Advances in neural information processing systems.
  10. Exact combinatorial optimization with graph convolutional neural networks. Advances in neural information pro-cessing systems.
  11. A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem. European Journal of Operational Research, 284(3): 814–833.
  12. Howard, R. A. 1960. Dynamic programming and markov processes. MIT Press and Wiley.
  13. Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR), 50(2): 1–35.
  14. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, 448–456. PMLR.
  15. Learning TSP requires rethinking generalization. International Conference on Principles and Practice of Constraint Programming (CP).
  16. Attention, learn to solve routing problems! International Conference on Learning Representations.
  17. Complexity of vehicle routing and scheduling problems. Networks, 11(2): 221–227.
  18. Learning to optimize. International Conference on Learning Representations.
  19. Bilevel learning for large-scale flexible flow shop scheduling. Computers & Industrial Engineering, 168: 108140.
  20. Learning to accelerate approximate methods for solving integer programming via early fixing. arXiv preprint arXiv:2207.02087.
  21. Iterated local search: Framework and applications. In Handbook of metaheuristics, 129–168. Springer.
  22. Augmented behavioral cloning from observation. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19).
  23. Reinforcement learning for solving the vehicle routing problem. arXiv preprint arXiv:1802.04240.
  24. A note on learning algorithms for quadratic assignment with graph neural networks. stat, 1050: 22.
  25. Solving permutation flowshop problem with deep reinforcement learning. In 2020 Prognostics and Health Management Conference (PHM-Besançon), 349–353. IEEE.
  26. Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling. IEEE Transactions on Emerging Topics in Computational Intelligence.
  27. Reinelt, G. 2003. The traveling salesman: computational solutions for TSP applications, volume 840. Springer.
  28. Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network. Alexandria Engineering Journal, 60(3): 2787–2800.
  29. Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8): 1439–1454.
  30. Computational experience with a branch-and-cut algorithm for flowshop scheduling with setups. Computers and Operations Research, 25(5): 351–366.
  31. Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega, 83: 213–222.
  32. An improved NEH heuristic to minimize makespan for flow shop scheduling problems. Decision Science Letters, 10(3): 311–322.
  33. Complexity of flow shop scheduling problems with transportation constraints. European Journal of Operational Research, 161(1): 32–41.
  34. Taillard, E. 1993. Benchmarks for basic scheduling problems. european journal of operational research, 64(2): 278–285.
  35. Behavioral cloning from observation. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).
  36. Recent advances in imitation learning from observation. arXiv preprint arXiv:1905.13566.
  37. New hard benchmark for flowshop scheduling problems minimising makespan. European Journal of Operational Research, 240(3): 666–677.
  38. Attention is all you need. In Advances in neural information processing systems, 5998–6008.
  39. Pointer networks. Advances in neural information processing systems.
  40. Zabinsky, Z. B.; et al. 2009. Random search algorithms. Department of Industrial and Systems Engineering, University of Washington, USA.
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