ABSTRACT
Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS simulations. Taking k-nearest neighbour with phenotypic characterisations of GP individuals as a surrogate approach, has been successfully used to preselect GP offspring to the next generation for effectiveness improvement. However, this approach is not straightforward to improve the training efficiency, which is normally the primary goal of surrogate. In addition, there is no study on which GP individuals (samples) are good for building surrogate models. To this end, first, this paper proposes a surrogate-assisted GP algorithm to reduce the training time of learning scheduling heuristics for DFJSS. Second, this paper further proposes an effective sampling strategy for surrogate-assisted GP. The results show that our proposed algorithm can achieve comparable performance with only about a third of training time of traditional GP. With the same training time, the proposed algorithm can significantly improve the quality of learned scheduling heuristics in all examined scenarios. Furthermore, the evolved scheduling heuristics by the proposed sample-aware surrogate-assisted GP are more interpretable with smaller rule sizes than traditional GP.
- Jürgen Branke, Torsten Hildebrandt, and Bernd Scholz-Reiter. 2015. Hyper-heuristic Evolution of dispatching rules: a comparison of rule representations. Evolutionary Computation 23, 2 (2015), 249--277.Google ScholarDigital Library
- Peter Brucker and Rainer Schlie. 1990. Job-shop scheduling with multi-purpose machines. Computing 45, 4 (1990), 369--375.Google ScholarDigital Library
- Marko Durasevic and Domagoj Jakobovic. 2018. Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment. Genetic Programming and Evolvable Machines 19, 1--2 (2018), 9--51.Google ScholarDigital Library
- Marko Durasevic and Domagoj Jakobovic. 2018. A survey of dispatching rules for the dynamic unrelated machines environment. Expert Systems with Applications 113 (2018), 555--569.Google ScholarDigital Library
- Martin Ester, Hans Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, Vol. 96. 226--231.Google Scholar
- Emma Hart, Peter Ross, and David Corne. 2005. Evolutionary scheduling: A review. Genetic Programming and Evolvable Machines 6, 2 (2005), 191--220.Google ScholarDigital Library
- Reinhard Haupt. 1989. A survey of priority rule-based scheduling. Operations-Research-Spektrum 11, 1 (1989), 3--16.Google ScholarCross Ref
- Torsten Hildebrandt and Jürgen Branke. 2015. On Using Surrogates with Genetic Programming. Evolutionary Computation 23, 3 (2015), 343--367.Google ScholarDigital Library
- Torsten Hildebrandt, Jens Heger, and Bernd Scholz Reiter. 2010. Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In Proceedings of the Conference on Genetic and Evolutionary Computation. ACM, 257--264.Google ScholarDigital Library
- Kristijan Jaklinović, Marko Ðurasević, and Domagoj Jakobović. 2021. Designing dispatching rules with genetic programming for the unrelated machines environment with constraints. Expert Systems with Applications 172 (2021), 114548.Google ScholarCross Ref
- Yaochu Jin. 2011. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation 1, 2 (2011), 61--70.Google ScholarCross Ref
- Yaochu Jin, Handing Wang, and Chaoli Sun. 2021. Data-driven evolutionary optimization.Google Scholar
- John R Koza. 1994. Genetic programming as a means for programming computers by natural selection. Statistics and Computing 4, 2 (1994), 87--112.Google ScholarCross Ref
- Matheus E Leusin, Enzo M Frazzon, Mauricio Uriona Maldonado, Mirko Kück, and Michael Freitag. 2018. Solving the job-shop scheduling problem in the industry 4.0 era. Technologies 6, 4 (2018), 107.Google ScholarCross Ref
- Leilei Liu, Yungming Cheng, and Xiaomi Wang. 2017. Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes. Landslides 14, 2 (2017), 535--546.Google ScholarCross Ref
- Su Nguyen, Mengjie Zhang, Damminda Alahakoon, and Kay Chen Tan. 2019. People-centric evolutionary system for dynamic production scheduling. IEEE transactions on cybernetics 51, 3 (2019), 1403--1416.Google Scholar
- Su Nguyen, 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 (2017), 2951--2965.Google ScholarCross Ref
- Li Nie, 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 (2013), 763--774.Google ScholarDigital Library
- Nelishia Pillay and Rong Qu. 2018. Hyper-heuristics: theory and applications. Springer.Google Scholar
- Marko Ðurasević and Domagoj Jakobović. 2022. Selection of dispatching rules evolved by genetic programming in dynamic unrelated machines scheduling based on problem characteristics. Journal of Computational Science 61 (2022), 101649.Google ScholarCross Ref
- Marko Ðurasević, Lucija Planinić, Francisco Javier Gil Gala, and Domagoj Jakobović. 2022. Novel ensemble collaboration method for dynamic scheduling problems. In Proceedings of the Genetic and Evolutionary Computation Conference. 893--901.Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, Su Nguyen, Kay Chen Tan, and Mengjie Zhang. 2022. Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling. IEEE Transactions on Evolutionary Computation (2022). Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2020. Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling. In Proceedings of the European Conference on Genetic Programming. Springer, 262--278.Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2021. Correlation Coefficient based Recombinative Guidance for Genetic Programming Hyper-heuristics in Dynamic Flexible Job Shop Scheduling. IEEE Transactions on Evolutionary Computation 25 (2021), 552--566.Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2022. Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling. IEEE Transactions on Cybernetics (2022). Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, and Mengjie Zhang. 2023. Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling. IEEE Transactions on Evolutionary Computation (2023). Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang, and Kay Chen Tan. 2021. Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling. IEEE Transactions on Evolutionary Computation 25, 4 (2021), 651--665.Google ScholarDigital Library
- Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2018. Genetic programming with multi-tree representation for dynamic flexible job shop scheduling. In AI 2018: Advances in Artificial Intelligence: 31st Australasian Joint Conference, Wellington, New Zealand, December 11--14, 2018, Proceedings 31. Springer, 472--484.Google ScholarCross Ref
- Fangfang Zhang, Yi Mei, and Mengjie Zhang. 2018. Surrogate-assisted genetic programming for dynamic flexible job shop scheduling. In Proceedings of the Australasian Joint Conference on Artificial Intelligence. Springer, 766--772.Google ScholarCross Ref
- Yong Zhou, Jianjun 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 (2020), 2561--2580.Google ScholarCross Ref
Index Terms
- Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling
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