References
- Akram, Kashif, Khurram Kamal, and Alam Zeb. 2016. “Fast Simulated Annealing Hybridized with Quenching for Solving Job Shop Scheduling Problem.” Applied Soft Computing 49: 510–523. doi:https://doi.org/10.1016/j.asoc.2016.08.037.
- Branke, Jürgen, Torsten Hildebrandt, and Bernd Scholz-Reiter. 2015. “Hyper-Heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.” Evolutionary Computation 23 (2): 249–277. doi:https://doi.org/10.1162/EVCO_a_00131.
- Branke, Jurgen, Su Nguyen, Christoph W. Pickardt, and Mengjie Zhang. 2016. “Automated Design of Production Scheduling Heuristics: A Review.” IEEE Transactions on Evolutionary Computation 20 (1): 110–124. doi:https://doi.org/10.1109/TEVC.2015.2429314.
- Burke, Edmund K., Michel Gendreau, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, and Rong Qu. 2013. “Hyper-Heuristics: A Survey of the State of the Art.” Journal of the Operational Research Society 64 (12): 1695–1724. doi:https://doi.org/10.1057/jors.2013.71.
- Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197. doi:https://doi.org/10.1109/4235.996017.
- Dominic, P. D. D., S. Kaliyamoorthy, and M. Saravana Kumar. 2004. “Efficient Dispatching Rules for Dynamic Job Shop Scheduling.” International Journal of Advanced Manufacturing Technology 24 (1–2): 70–75. doi:https://doi.org/10.1007/s00170-002-1534-5.
- Drake, John H., Ahmed Kheiri, Ender Özcan, and Edmund K. Burke. 2020. “Recent Advances in Selection Hyper-Heuristics.” European Journal of Operational Research 285 (2): 405–428.
- Flórez, Edson, Wilfredo Gómez, and Lola Bautista. 2013. “An Ant Colony Optimization Algorithm for Job Shop Scheduling Problem.” Applied Mechanics and Materials 321–324: 2116–2121.
- Friedlander, Anna, Kourosh Neshatian, and Mengjie Zhang. 2011. “Meta-Learning and Feature Ranking Using Genetic Programming for Classification: Variable Terminal Weighting.” In 2011 IEEE Congress of Evolutionary Computation, CEC 2011, 941–948. doi:https://doi.org/10.1109/CEC.2011.5949719.
- Geiger, Christopher D., Reha Uzsoy, and Haldun Aytuğ. 2006. “Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach.” Journal of Scheduling 9 (1): 7–34. doi:https://doi.org/10.1007/s10951-006-5591-8.
- Hildebrandt, Torsten, and Jürgen Branke. 2015. “On Using Surrogates with Genetic Programming.” Evolutionary Computation 23 (3): 343–367. doi:https://doi.org/10.1162/EVCO_a_00133.
- Hildebrandt, Torsten, Jens Heger, and Bernd Scholz-Reiter. 2010. “Towards Improved Dispatching Rules for Complex Shop Floor Scenarios – A Genetic Programming Approach.” In Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO ‘10, 257–264. New York, New York, USA: ACM Press. doi:https://doi.org/10.1145/1830483.1830530.
- Hunt, Rachel, Johnston Richard, and Mengjie Zhang. 2016. Evolving Dispatching Rules with Greater Understandability for Dynamic Job Shop Scheduling Mark Johnston. Wellington: School of Engineering and Computer Science, Victoria University of Wellington.
- Koza, J. R. 1994. Genetic Programming II: Automatic Discovery of Reusable Subprograms. Vol. 13 (8). Cambridge, MA. http://www.cs.bham.ac.uk/∼wbl/ftp/ftp.io.com/papers/jaws2ann.txt.
- Mei, Yi, Su Nguyen, Bing Xue, and Mengjie Zhang. 2017. “An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming.” IEEE Transactions on Emerging Topics in Computational Intelligence 1 (5): 339–353. doi:https://doi.org/10.1109/tetci.2017.2743758.
- Mei, Yi, Su Nguyen, and Mengjie Zhang. 2017. “Constrained Dimensionally Aware Genetic Programming for Evolving Interpretable Dispatching Rules in Dynamic Job Shop Scheduling.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10593 LNCS:435–447. Springer Verlag. doi:https://doi.org/10.1007/978-3-319-68759-9_36.
- Mei, Yi, Mengjie Zhang, and Su Nyugen. 2016. “Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming.” In GECCO 2016 – Proceedings of the 2016 Genetic and Evolutionary Computation Conference, 365–372. New York, NY, USA: Association for Computing Machinery, Inc. doi:https://doi.org/10.1145/2908812.2908822.
- Miller, Julian F., and Peter Thomson. 2000. “Cartesian Genetic Programming.” In European Conference on Genetic Programming, 1802: 121–132. Berlin: Springer Verlag. doi:https://doi.org/10.1007/978-3-540-46239-2_9.
- Mohan, Jatoth, Krishnanand Lanka, and A. Neelakanteswara Rao. 2019. “A Review of Dynamic Job Shop Scheduling Techniques.” Procedia Manufacturing 30: 34–39. doi:https://doi.org/10.1016/j.promfg.2019.02.006.
- Nguyen, Su, Yi Mei, Bing Xue, and Mengjie Zhang. 2018. “A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.” Evolutionary Computation 27 (3): 467–596. doi:https://doi.org/10.1162/evco_a_00230.
- Nguyen, Su, Yi Mei, and Mengjie Zhang. 2017. “Genetic Programming for Production Scheduling: A Survey with a Unified Framework.” Complex & Intelligent Systems 3 (1): 41–66. doi:https://doi.org/10.1007/s40747-017-0036-x.
- Nguyen, Su, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. 2013. “Dynamic Multi-Objective Job Shop Scheduling: A Genetic Programming Approach.” Studies in Computational Intelligence 505: 251–282. doi:https://doi.org/10.1007/978-3-642-39304-4_10.
- Nguyen, Su, Mengjie Zhang, and Kay Chen Tan. 2015. “Enhancing Genetic Programming Based Hyper-Heuristics for Dynamic Multi-Objective Job Shop Scheduling Problems.” In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 – Proceedings, 2781–2788: Institute of Electrical and Electronics Engineers Inc. doi:https://doi.org/10.1109/CEC.2015.7257234.
- Nguyen, Su, Mengjie Zhang, and Kay Chen Tan. 2017. “Surrogate-Assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules.” IEEE Transactions on Cybernetics 47 (9): 2951–2965. doi:https://doi.org/10.1109/TCYB.2016.2562674.
- Nie, Li, Liang Gao, Peigen Li, and Xinyu Li. 2013. “A GEP-Based Reactive Scheduling Policies Constructing Approach for Dynamic Flexible Job Shop Scheduling Problem with Job Release Dates.” Journal of Intelligent Manufacturing 24 (4): 763–774. doi:https://doi.org/10.1007/s10845-012-0626-9.
- Nie, Li, Liang Gao, Peigen Li, and Liping Zhang. 2011. “Application of Gene Expression Programming on Dynamic Job Shop Scheduling Problem.” In Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2011, 291–295. doi:https://doi.org/10.1109/CSCWD.2011.5960088.
- Park, Byung Joo, Hyung Rim Choi, and Hyun Soo Kim. 2003. “A Hybrid Genetic Algorithm for the Job Shop Scheduling Problems.” Computers & Industrial Engineering 45 (4): 597–613. doi:https://doi.org/10.1016/S0360-8352(03)00077-9.
- Riley, M., Y. Mei, and M. Zhang. 2016. “Improving Job Shop Dispatching Rules via Terminal Weighting and Adaptive Mutation in Genetic Programming.” In 2016 IEEE Congress on Evolutionary Computation (CEC), 3362–3369. doi:https://doi.org/10.1109/CEC.2016.7744215.
- Sels, Veronique, Nele Gheysen, and Mario Vanhoucke. 2012. “A Comparison of Priority Rules for the Job Shop Scheduling Problem under Different Flow Time- and Tardiness-Related Objective Functions.” International Journal of Production Research 50 (15): 4255–4270. doi:https://doi.org/10.1080/00207543.2011.611539.
- Shady, Salama, Toshiya Kaihara, Nobutada Fujii, and Daisuke Kokuryo. 2020a. “A Hyper-Heuristic Framework Using GP for Dynamic Job Shop Scheduling Problem.” In Proceedings of the 64th Annual Conference of the Institute of Systems, Control and Information Engineers (ISCIE), 248–252.
- Shady, Salama, Toshiya Kaihara, Nobutada Fujii, and Daisuke Kokuryo. 2020b. “Automatic Design of Dispatching Rules with Genetic Programming for Dynamic Job Shop Scheduling.” In IFIP International Conference on Advances in Production Management Systems, 591 IFIP:399–407. Springer. doi:https://doi.org/10.1007/978-3-030-57993-7_45.
- Shady, Salama, Toshiya Kaihara, Nobutada Fujii, and Daisuke Kokuryo. 2020c. “A Proposal on Dispatching Rule Generation Mechanism Using GP for Dynamic Job Shop Scheduling with Machine Breakdowns.” In Scheduling Symposium 2020, 155–160. Osaka. https://www.researchgate.net/publication/344296207.
- Zhang, Fangfang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2021. “Evolving Scheduling Heuristics via Genetic Programming with Feature Selection in Dynamic Flexible Job-Shop Scheduling.” IEEE Transactions on Cybernetics 51 (4): 1797–1811. doi:https://doi.org/10.1109/TCYB.2020.3024849.
- Zhang, Fangfang, Yi Mei, and Mengjie Zhang. 2019. “A Two-Stage Genetic Programming Hyper-Heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling.” In GECCO 2019 – Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 347–355. New York, NY, USA: Association for Computing Machinery, Inc. doi:https://doi.org/10.1145/3321707.3321790.
- Zhou, Yong, and Jian Jun Yang. 2019. “Automatic Design of Scheduling Policies for Dynamic Flexible Job Shop Scheduling by Multi-Objective Genetic Programming Based Hyper-Heuristic.” Procedia CIRP 79: 439–444. doi:https://doi.org/10.1016/j.procir.2019.02.118.
- Zhou, Yong, Jian-jun Yang, and Zhuang Huang. 2020. “Automatic Design of Scheduling Policies for Dynamic Flexible Job Shop Scheduling via Surrogate-Assisted Cooperative Co-Evolution Genetic Programming.” International Journal of Production Research 58 (9): 2561–2580. doi:https://doi.org/10.1080/00207543.2019.1620362.
- Zhou, Yong, Jian Jun Yang, and Lian Yu Zheng. 2019. “Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling.” IEEE Access 7: 68–88. doi:https://doi.org/10.1109/ACCESS.2018.2883802.