Abstract
This paper introduces a framework in which dispatching rules for job-shop scheduling problems are discovered by analysing the characteristics of optimal solutions. Training data is created via randomly generated job-shop problem instances and their corresponding optimal solution. Linear classification is applied in order to identify good choices from worse ones, at each dispatching time step, in a supervised learning fashion. The method is purely data-driven, thus less problem specific insights are needed from the human heuristic algorithm designer. Experimental studies show that the learned linear priority dispatching rules outperforms common single priority dispatching rules, with respect to minimum makespan.
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References
Zhang, W., Dietterich, T.G.: A Reinforcement Learning Approach to Job-shop Scheduling. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1114–1120. Morgan Kaufmann, San Francisco (1995)
Tay, J., Ho, N.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54(3), 453–473 (2008)
Li, X., Olafsson, S.: Discovering Dispatching Rules Using Data Mining. Journal of Scheduling 8(6), 515–527 (2005)
Malik, A.M., Russell, T., Chase, M., Beek, P.: Learning heuristics for basic block instruction scheduling. Journal of Heuristics 14(6), 549–569 (2007)
Garey, M., Johnson, D., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1(2), 117–129 (1976)
Panwalkar, S., Iskander, W.: A Survey of Scheduling Rules. Operations Research 25(1), 45–61 (1977)
Russell, T., Malik, A.M., Chase, M., van Beek, P.: Learning Heuristics for the Superblock Instruction Scheduling Problem. IEEE Transactions on Knowledge and Data Engineering 21(10), 1489–1502 (2009)
Burke, E., Petrovic, S., Qu, R.: Case-based heuristic selection for timetabling problems. Journal of Scheduling 9(2), 115–132 (2006)
Jayamohan, M.: Development and analysis of cost-based dispatching rules for job shop scheduling. European Journal of Operational Research 157(2), 307–321 (2004)
Makhorin, A.: GNU linear programming kit. Moscow Aviation Institute, Moscow, Russia, 38 (May 2009), Software available at http://www.gnu.org/software/glpk/glpk.html
Fan, R.e., Wang, X.r., Lin, C.j.: LIBLINEAR: A Library for Large Linear Classification. Corpus 9, 1871–1874 (2008), Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear
Lin, C.j., Weng, R.C.: Trust Region Newton Method for Large-Scale Logistic Regression. Journal of Machine Learning Research 9, 627–650 (2008)
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Ingimundardottir, H., Runarsson, T.P. (2011). Supervised Learning Linear Priority Dispatch Rules for Job-Shop Scheduling. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_20
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DOI: https://doi.org/10.1007/978-3-642-25566-3_20
Publisher Name: Springer, Berlin, Heidelberg
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