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Approximation of dispatching rules for manufacturing simulation using data mining methods.

, , and . WSC, page 2329-2340. IEEE/ACM, (2015)

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Development of an Integrated Solution for Data Farming and Knowledge Discovery in Simulation Data., , , , and . Simul. Notes Eur., 32 (3): 121-126 (2022)Simulation-Based Deep Reinforcement Learning For Modular Production Systems., , and . WSC, page 1596-1607. IEEE, (2020)Approximation of dispatching rules for manufacturing simulation using data mining methods., , and . WSC, page 2329-2340. IEEE/ACM, (2015)Approximation of Dispatching Rules in Manufacturing Control Using Artificial Neural Networks., and . PADS, page 1-8. IEEE Computer Society, (2011)A Method Using Generative Adversarial Networks for Robustness Optimization., , , and . ACM Trans. Model. Comput. Simul., 32 (2): 12:1-12:22 (2022)Knowledge Discovery in Simulation Data., , and . ACM Trans. Model. Comput. Simul., 30 (4): 24:1-24:25 (2020)Knowledge Discovery in Scheduling Systems Using Evolutionary Bilevel Optimization and Visual Analytics., , , and . EMO, volume 11411 of Lecture Notes in Computer Science, page 439-450. Springer, (2019)A Method for Robustness Optimization Using Generative Adversarial Networks., , , and . SIGSIM-PADS, page 1-10. ACM, (2020)Knowledge discovery in simulation data: A case study of a gold mining facility., , , and . WSC, page 1607-1618. IEEE, (2016)Online Analysis of Simulation Data with Stream-based Data Mining., , and . SIGSIM-PADS, page 241-248. ACM, (2017)