448
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Evolutionary job scheduling with optimized population by deep reinforcement learning

, , &
Pages 494-509 | Received 31 Jul 2021, Accepted 25 Nov 2021, Published online: 22 Dec 2021

References

  • Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv:1409.0473.
  • Barto, Andrew G., Richard S. Sutton, and Charles W. Anderson. 1983. “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems.” IEEE Transactions on Systems, Man, and Cybernetics SMC-13 (5): 834–846.
  • Bello, Irwan, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio. 2016. “Neural Combinatorial Optimization with Reinforcement Learning.” arXiv:1611.09940.
  • Bitam, Salim, Sherali Zeadally, and Abdelhamid Mellouk. 2018. “Fog Computing Job Scheduling Optimization Based on Bees Swarm.” Enterprise Information Systems 12 (4): 373–397.
  • Bye, Robin T., Magnus Gribbestad, Ramesh Chandra, and Ottar L. Osen. 2021. “A Comparison of GA Crossover and Mutation Methods for the Traveling Salesman Problem.” In Proceedings of Innovations in Computational Intelligence and Computer Vision (ICICV 2020), 529–542. Singapore: Springer. doi:10.1007/978-981-15-6067-5_60.
  • Cao, ZhengCai, ChengRan Lin, and MengChu Zhou. 2019. “A Knowledge-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop with Sequencing Flexibility.” IEEE Transactions on Automation Science and Engineering 18 (1): 56–69. doi:10.1109/TASE.2019.2945717
  • Cho, Kyunghyun, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches.” arXiv:1409.1259.
  • Feng, Hanxin, Changbai Tan, Tangbin Xia, Ershun Pan, and Lifeng Xi. 2019. “Joint Optimization of Preventive Maintenance and Flexible Flowshop Sequence-Dependent Group Scheduling Considering Multiple Setups.” Engineering Optimization 51 (9): 1529–1546.
  • Hu, Zhiming, Jinlong Zhang, Huiming Ma, Jie Xiong, and Shuhong Zhao. 2021. “Vertical Synergy Analysis of China's Manufacturing Industry Transformation and Upgrading Policies.” Science and Technology Progress and Policy 37 (1): 122–128.
  • Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. 1996. “Reinforcement Learning: A Survey.” Journal of Artificial Intelligence Research 4: 237–285.
  • Kool, Wouter, Herke Van Hoof, and Max Welling. 2018. “Attention, Learn to Solve Routing Problems!” arXiv:1803.08475
  • Leu, Sou-Sen, and Shao-Ting Hwang. 2001. “A GA-Based Model for Maximizing Precast Plant Production Under Resource Constraints.” Engineering Optimization 33 (5): 619–642.
  • Li Kai-Wen, Zhang Tao, Wang Rui, Qin Wei-Jian, He Hui-Hui, Huang Hong. Research reviews of combinatorial optimization methods based on deep reinforcement learning. Acta Automatica Sinica, 2021, 47(11): 2521–2537. doi: 10.16383/j.aas.c200551
  • Lim, Dudy, Yaochu Jin, Yew-Soon Ong, and Bernhard Sendhoff. 2009. “Generalizing Surrogate-Assisted Evolutionary Computation.” IEEE Transactions on Evolutionary Computation 14 (3): 329–355.
  • Liu, Junjun, and Wenzheng Li. 2018. “Greedy Permuting Method for Genetic Algorithm on Traveling Salesman Problem.” In Proceedings of the IEEE 8th International Conference on Electronics Information and Emergency Communication (ICEIEC 2018), 47–51. Piscataway, NJ: IEEE.
  • Liu, Lemao, Masao Utiyama, Andrew Finch, and Eiichiro Sumita. 2016. “Neural Machine Translation with Supervised Attention.” arXiv:1609.04186
  • Liu, Xinwang, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggang Shen, Jianping Yin, and Wen Gao. 2019. “Multiple Kernel k-Means with Incomplete Kernels.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (5): 1191–1204. doi:10.1109/TPAMI.2019.2892416
  • Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. 2015. “Effective Approaches to Attention-Based Neural Machine Translation.” arXiv:1508.04025
  • Mirjalili, Seyedali, and Andrew Lewis. 2016. “The Whale Optimization Algorithm.” Advances in Engineering Software 95: 51–67.
  • Mnih, Volodymyr, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. “Asynchronous Methods for Deep Reinforcement Learning.” In Proceedings of the 33rd International Conference on Machine Learning, PMLR 48: 1928–1937. http://proceedings.mlr.press/v48/mniha16.html
  • Park, Daniel S., William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le. 2019. “Specaugment: A Simple Data Augmentation Method for Automatic Speech Recognition.” arXiv:1904.08779
  • Ravanelli, Mirco, Titouan Parcollet, and Yoshua Bengio. 2019. “The Pytorch-Kaldi Speech Recognition Toolkit.” In Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6465–6469. Piscataway, NJ: IEEE.
  • Saragih, Nova Indah, Senator Nur Bahagia, Suprayogi Suprayogi, and Ibnu Syabri. 2019. “A Heuristic Method for Location–Inventory–Routing Problem in a Three-Echelon Supply Chain System.” Computers & Industrial Engineering 127: 875–886. doi:10.1016/j.cie.2018.11.026
  • Sebt, M. H., M. R. Afshar, and Y. Alipouri. 2017. “Hybridization of Genetic Algorithm and Fully Informed Particle Swarm for Solving the Multi-Mode Resource-Constrained Project Scheduling Problem.” Engineering Optimization 49 (3): 513–530. doi:10.1080/0305215X.2016.1197610
  • Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. 2015. “Pointer Networks.” arXiv:1506.03134
  • Wang, Qiang, Jiaqing Xu, Rongchun Li, Peng Qiao, Ke Yang, Shijie Li, and Yong Dou. 2018. “Deep Image Clustering Using Convolutional Autoencoder Embedding with Inception-Like Block.” In Proceedings of the 25th IEEE International Conference on Image Processing (ICIP 2018), 2356–2360. Piscataway, NJ: IEEE.
  • Wu, Di, and G. Gary Wang. 2021. “Employing Reinforcement Learning to Enhance Particle Swarm Optimization Methods.” Engineering Optimization, 1–20. Advance online publication. doi:10.1080/0305215X.2020.1867120 .
  • Yamada, Takeshi, and Ryohei Nakano. 1996. “Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search.” In Meta-Heuristics: Theory and Applications, 237–248. Boston, MA: Kluwer Academic. doi:10.1007/978-1-4613-1361-8.
  • Yu, Vincent F., Sesya Sri Purwanti, A. A. N. Perwira Redi, Chung-Cheng Lu, Suprayogi Suprayogi, and Parida Jewpanya. 2018. “Simulated Annealing Heuristic for the General Share-a-Ride Problem.” Engineering Optimization 50 (7): 1178–1197.
  • Zhang, J., and Jie-Sheng Wang. 2020. “Improved SALP Swarm Algorithm Based on Levy Flight and Sine Cosine Operator.” IEEE Access 8: 99740–99771.
  • Zhang, Tie, Caicheng Wu, Yingwu He, Yanbiao Zou, and Cailei Liao. 2021. “Gain Parameters Optimization Strategy of Cross-Coupled Controller Based on Deep Reinforcement Learning.” Engineering Optimization, 1–16. Advance online publication. doi:10.1080/0305215X.2021.1897801 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.