References
- Baek, D. H., and W. C. Yoon. 2002. “Co-evolutionary Genetic Algorithm for Multi-Machine Scheduling: Coping with High Performance Variability.” International Journal of Production Research 40 (1): 239–254. doi: 10.1080/00207540110079419
- Branke, J., T. Hildebrandt, and B. Scholz-Reiter. 2015. “Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.” Evolutionary Computation 23 (2): 249–277. doi: 10.1162/EVCO_a_00131
- Branke, J., S. Nguyen, C. W. Pickardt, and M. Zhang. 2016. “Automated Design of Production Scheduling Heuristics: A Review.” IEEE Transactions on Evolutionary Computation 20 (1): 110–124. doi: 10.1109/TEVC.2015.2429314
- Brockhoff, D., A. Auger, N. Hansen, D. V. Arnold, and T. Hohm. 2010. “Mirrored Sampling and Sequential Selection for Evolution Strategies.” International Conference on Parallel Problem Solving from Nature, 11–21.
- Burke, E. K., M. Hyde, G. Kendall, G. Ochoa, E. Özcan, and J. R. Woodward. 2010. “A Classification of Hyper-Heuristic Approaches.” In Handbook of Metaheuristics, edited by M. Geadreau and J.-Y. Potvin, 449–468. Springer.
- Duchi, J., E. Hazan, and Y. Singer. 2011. “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.” Journal of machine learning research 12 (7): 2121–2159.
- El-Bouri, A., S. Balakrishnan, and N. Popplewell. 2000. “Sequencing Jobs on a Single Machine: A Neural Network Approach.” European Journal of Operational Research 126 (3): 474–490. doi: 10.1016/S0377-2217(99)00302-1
- Fu, M. C. 2001. “Simulation Optimization,” Proceedings of the 2001 Winter Simulation Conference, 53–61.
- Fu, M. C. 2006. “Gradient Estimation.” In Handbooks in Operations Research and Management Science, edited by S. G. Henderson and B. L. Nelson, 575–616. New York: Elsevier.
- Geiger, C. D., R. Uzsoy, and H. Aytuğ. 2006. “Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach.” Journal of Scheduling 9 (1): 7–34. doi: 10.1007/s10951-006-5591-8
- Hong, T. Y., and C. F. Chien. 2020. “A Simulation-Based Dynamic Scheduling and Dispatching System with Multi-Criteria Performance Evaluation for Industry 3.5 and an Empirical Study for Sustainable TFT-LCD Array Manufacturing.” International Journal of Production Research 58 (24): 7531–7547. doi: 10.1080/00207543.2020.1777342
- Hunt, R., M. Johnston, and M. Zhang. 2014. “Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop Using Genetic Programming.” IEEE Congress on Evolutionary Computation (CEC).
- Ingimundardottir, H., and T. P. Runarsson. 2011. “Supervised Learning Linear Priority Dispatch Rules for job-Shop Scheduling.” International Conference on Learning and Intelligent Optimization, 263–277. doi: 10.1007/978-3-642-25566-3_20
- Jakobović, D., and L. Budin. 2006. “Dynamic Scheduling with Genetic Programming.” European Conference on Genetic Programming, 73–84.
- Jun, S., and S. Lee. 2020. “Learning Dispatching Rules for Single Machine Scheduling with Dynamic Arrivals based on Decision Trees and Feature Construction.” International Journal of Production Research 59 (9): 1–19. doi: 10.1080/00207543.2020.1779371
- Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv Preprint ArXiv 1412: 6980.
- Koza, J. R., and J. R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection Vol. 1. Cambridge, MA: MIT press.
- Kück, M., E. Broda, M. Freitag, T. Hildebrandt, and E. M. Frazzon. 2017. “Towards Adaptive Simulation-Based Optimization to Select Individual Dispatching Rules for Production Control.” Winter Simulation Conference (WSC).
- Lee, J. H., and H. J. Kim. 2021. “Reinforcement Learning for Robotic Flow Shop Scheduling with Processing Time Variations.” International Journal of Production Research, 1–23. doi: 10.1080/00207543.2021.1887533
- Li, X., and S. Olafsson. 2005. “Discovering Dispatching Rules Using Data Mining.” Journal of Scheduling 8 (6): 515–527. doi: 10.1007/s10951-005-4781-0
- Miyashita, K. 2000. “Job-Shop Scheduling with Genetic Programming,” Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, 505–512.
- Mönch, L., J. W. Fowler, S. Dauzère-Pérès, S. J. Mason, and O. Rose. 2011. “A Survey of Problems, Solution Techniques, and Future Challenges in Scheduling Semiconductor Manufacturing Operations.” Journal of Scheduling 14 (6): 583–599. doi: 10.1007/s10951-010-0222-9
- Nasiri, M. M., R. Yazdanparast, and F. Jolai. 2017. “A Simulation Optimisation Approach for Real-time Scheduling in an Open Shop Environment using a Composite Dispatching Rule.” International Journal of Computer Integrated Manufacturing 30 (12): 1239–1252. doi: 10.1080/0951192X.2017.1307452
- Nguyen, S., Y. Mei, and M. Zhang. 2017. “Genetic Programming for Production Scheduling: A Survey with a Unified Framework.” Complex & Intelligent Systems 3 (1): 41–66. doi: 10.1007/s40747-017-0036-x
- Olafsson, S., and X. Li. 2010. “Learning Effective new Single Machine Dispatching Rules from Optimal Scheduling Data.” International Journal of Production Economics 128 (1): 118–126. doi: 10.1016/j.ijpe.2010.06.004
- Ouelhadj, D., and S. Petrovic. 2009. “A Survey of Dynamic Scheduling in Manufacturing Systems.” Journal of Scheduling 12 (4): 417. doi: 10.1007/s10951-008-0090-8
- Pickardt, C. W., T. Hildebrandt, J. Branke, J. Heger, and B. Scholz-Reiter. 2013. “Evolutionary Generation of Dispatching Rule Sets for Complex Dynamic Scheduling Problems.” International Journal of Production Economics 145 (1): 67–77. doi: 10.1016/j.ijpe.2012.10.016
- Salimans, T., J. Ho, X. Chen, S. Sidor, and I. Sutskever. 2017. “Evolution Strategies as a Scalable Alternative to Reinforcement Learning.” ArXiv: 1703.03864.
- Sarin, S. C., A. Varadarajan, and L. Wang. 2011. “A Survey of Dispatching Rules for Operational Control in Wafer Fabrication.” Production Planning and Control 22 (1): 4–24. doi: 10.1080/09537287.2010.490014
- Schneckenreither, M., S. Haeussler, and C. Gerhold. 2020. “Order Release Planning with Predictive Lead Times: a Machine Learning Approach.” International Journal of Production Research 59 (11): 1–19.
- Sehnke, F., C. Osendorfer, T. Rückstieß, A. Graves, J. Peters, and J. Schmidhuber. 2010. “Parameter-exploring Policy Gradients.” Neural Networks 23 (4): 551–559. doi: 10.1016/j.neunet.2009.12.004
- Staines, J., and D. Barber. 2013. Optimization by Variational Bounding. 21st European Symposium on Artificial Neural Networks, Bruges, Belgium.
- Tan, B., and S. Khayyati. 2021. “Supervised Learning-Based Approximation Method for Single-Server Open Queueing Networks with Correlated Interarrival and Service Times.” International Journal of Production Research, 1–26.
- Weckman, G. R., C. V. Ganduri, and D. A. Koonce. 2008. “A Neural Network job-Shop Scheduler.” Journal of Intelligent Manufacturing 19 (2): 191–201. doi: 10.1007/s10845-008-0073-9
- Wierstra, D., T. Schaul, T. Glasmachers, Y. Sun, J. Peters, and J. Schmidhuber. 2014. “Natural Evolution Strategies.” The Journal of Machine Learning Research 15 (1): 949–980.
- Wu, C. H., F. Y. Zhou, C. K. Tsai, C. J. Yu, and S. Dauzère-Pérès. 2020. “A Deep Learning Approach for the Dynamic Dispatching of Unreliable Machines in re-Entrant Production Systems.” International Journal of Production Research 58 (9): 2822–2840. doi: 10.1080/00207543.2020.1727041
- Yang, T., Y. Kuo, and C. Cho. 2007. “A Genetic Algorithms Simulation Approach for the Multi-Attribute Combinatorial Dispatching Decision Problem.” European Journal of Operational Research 176 (3): 1859–1873. doi: 10.1016/j.ejor.2005.10.048