Abstract
Automated planning and scheduling continues to be an important part of artificial intelligence research and practice [6, 7, 11]. Many commonly-occurring scheduling settings include multiple stages and alternative resources, resulting in challenging combinatorial problems with high-dimensional solution spaces. The literature for solving such problems is dominated by specialized meta-heuristic algorithms.
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Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04244-7_14
Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S.: Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016)
Bengio, Y., et al.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)
Gupta, J.N., Sexton, R.S., Tunc, E.A.: Selecting scheduling heuristics using neural networks. INFORMS J. Comput. 12(2), 150–162 (2000)
Heching, A., Hooker, J.N.: Scheduling home hospice care with logic-based benders decomposition. In: Quimper, C.-G. (ed.) CPAIOR 2016. LNCS, vol. 9676, pp. 187–197. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33954-2_14
Ingimundardottir, H., Runarsson, T.P.: Discovering dispatching rules from data using imitation learning: a case study for the job-shop problem. J. Sched. 1–16 (2017)
Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)
Ruiz, R., Vázquez-Rodríguez, J.A.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205(1), 1–18 (2010)
Shahrabi, J., Adibi, M.A., Mahootchi, M.: A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput. Ind. Eng. 110, 75–82 (2017)
Tran, T.T., Vaquero, T., Nejat, G., Beck, J.C.: Robots in retirement homes: applying off-the-shelf planning and scheduling to a team of assistive robots. J. Artif. Intell. Res. 58, 523–590 (2017)
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Tan, Y. (2018). Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_36
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DOI: https://doi.org/10.1007/978-3-319-89656-4_36
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