1 |
H. Mobahi, R. Collobert, and J. Weston, "Deep learning from temporal coherence in video," in Proc. of the 26th International Conference On Machine Learning, ICML 2009, pp. 737-744, 2009.
|
2 |
W. Zhang and T. G Diettench, "A Reinforcemen t Learnin g Approac h t o Job-shop Scheduling," in Proc. of 14th Int. Jt Conf. Artif. Intell, pp. 1114-1120, 1995. [Online]. Available: https://www.ijcai.org/Proceedings/95-2/Papers/013.pdf.
|
3 |
M. E. Aydin and E. Oztemel, "Dynamic job-shop scheduling using reinforcement learning agents," Robotics and Autonomous Systems, vol. 33, no. 2, pp. 169-178, 2000.
DOI
|
4 |
V. Mnih, D. Silver, and M. Riedmiller, "Playing Atari with Deep Reinforcement Learning," arXiv:1312.5602 [cs. LG], pp. 1-9, 2013. [Online]. Available: https://arxiv.org/abs/1312.5602
|
5 |
Y. Ye et al., "A New Approach for Resource Scheduling with Deep Reinforcement Learning," arXiv:1806.08122 [cs.AI], pp. 2-6, 2018, [Online]. Available: http://arxiv.org/abs/1806.08122.
|
6 |
C. C. Lin, D. J. Deng, Y. L. Chih, and H. T. Chiu, "Smart Manufacturing Scheduling with Edge Computing Using Multiclass Deep Q Network," IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4276-4284, 2019.
DOI
|
7 |
X. Shao and C. S. O. O. Kim, "Multi-step Short-term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior," IEEE Access, vol. 8, pp. 125263-125273, 2020.
DOI
|
8 |
Y. P. S. Foo and T. Takefuji, "Integer linear programming neural networks for job-shop scheduling," in Proc. of IEEE Inernational Conference on Neural Networks, pp. 341-348, 1988.
|
9 |
Y. Fu, J. Ding, H. Wang, and J. Wang, "Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system," Applied Soft Computing Journal, vol. 68, pp. 847-855, 2019.
|
10 |
E. M. Frazzon, A. Albrecht, M. Pires, E. Israel, M. Kuck, and M. Freitag, "Hybrid approach for the integrated scheduling of production and transport processes along supply chains," International Journal of Production Research, vol. 56, no. 5, pp. 2019-2035, 2018.
DOI
|
11 |
Z. C. Lipton, J. Berkowitz, and C. Elkan, "A Critical Review of Recurrent Neural Networks for Sequence Learning," arXiv:1506.00019 [cs.LG], pp. 1-38, 2015. [Online]. Available: https://arxiv.org/abs/1506.00019.
|
12 |
X. Shao, C. PU, Y. ZHANG, and C. S. KIM, "Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting," IEEE Access, vol. 8, pp. 188352-188362, 2020.
DOI
|
13 |
G. L. Thompson, "Probabilistic learning combinations of local job-shop scheduling rules," Industrial Scheduling, vol. 3, no. 2, pp. 225-251, 1963.
|
14 |
M. Marti and A. Maki, "A multitask deep learning model for real-time deployment in embedded systems," 2017, [Online]. Available: http://arxiv.org/abs/1711.00146.
|
15 |
G. R. Weckman, C. V. Ganduri, and D. A. Koonce, "A neural network job-shop scheduler," Journal of Intelligent Manufacturing, vol. 19, no. 2, pp. 191-201, 2008.
DOI
|
16 |
K. Wang, H. Luo, F. Liu, and X. Yue, "Permutation Flow Shop Scheduling with Batch Delivery to Multiple Customers in Supply Chains," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 10, pp. 1826-1837, 2018.
DOI
|
17 |
H. Mao, M. Alizadeh, I. Menache, and S. Kandula, "Resource management with deep reinforcement learning," in Proc. of HotNets 2016 - Proceedings of the 15th ACM Workshop on Hot Topics in Networks, pp. 50-56, 2016.
|
18 |
Z. Zang et al., "Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation," Computational Intelligence and Neuroscience, vol. 2019, no. ii, 2019.
|
19 |
I. Gonzalez-Rodriguez, C. R. Vela, and J. Puente, "A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty," Journal of Intelligent Manufacturing, vol. 21, no. 1, pp. 65-73, 2010.
DOI
|
20 |
L. Asadzadeh, "A local search genetic algorithm for the job shop scheduling problem with intelligent agents," Computers and Industrial Engineering, vol. 85, pp. 376-383, 2015.
DOI
|
21 |
C. L. Liu, C. C. Chang, and C. J. Tseng, "Actor-critic deep reinforcement learning for solving job shop scheduling problems," IEEE Access, vol. 8, pp. 71752-71762, 2020.
DOI
|
22 |
Y. Bazi and F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 11, pp. 3374-3385, 2006.
DOI
|
23 |
H. W. Ge, L. Sun, Y. C. Liang, and F. Qian, "An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling," IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, vol. 38, no. 2, pp. 358-368, 2008.
DOI
|
24 |
S. Mathematics and N. May, "The Complexity of Flowshop and Jobshop Scheduling," Authors (s): M. R. Garey, D. S. Johnson and Ravi Sethi Published by: INFORMS Stable URL: http://www.jstor.org/stable/3689278 Accessed: 28-03-2016 13: 55 UTC Your use of the JSTOR archive indicat," vol. 1, no. 2, pp. 117-129, 2016.
|
25 |
M. Kurdi, "An effective new island model genetic algorithm for job shop scheduling problem," Computers and Operations Research, vol. 67, pp. 132-142, 2016.
DOI
|
26 |
Y. L. Pavlov, "Random forests," Machine Learning, vol. 45, pp. 5-32, 2019.
DOI
|
27 |
F. Zhao, Y. HongDong, C. Yahong, and M. YuXuhui, "Integration of Artificial Neural Networks and Genetic Algorithm for Job-Shop Scheduling Problem," in Proc. of International Symposium on Neural Networks-2005, pp. 770-775, 2005.
|
28 |
Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
DOI
|
29 |
K. Kim and J. Lee, "Deep CNN based Pilot Allocation Scheme in Massive MIMO systems," KSII Transactions on Internet and Information Systems, vol. 14, no. 10, pp. 4214-4230, 2020.
DOI
|
30 |
Xiaorui Shao, C.-S. Kim, and P. Sontakke, "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN-LSTM," Energies, vol. 13, no. 8, p. 1881, 2020.
DOI
|
31 |
S. Lawrence, "An Experimental Investigation of heuristic Scheduling Techniques," Supplement to Resource Constrained Project Scheduling, 1984.
|
32 |
T. Gabel and M. Riedmiller, "Adaptive Reactive Job-Shop Scheduling With Reinforcement Learning Agents," International Journal of Information Technology and Intelligent Computing, vol. 2, no. 4, 2008. [Online]. Available: http://ml.informatik.unifreiburg.de/_media/publications/gr07.pdf.
|
33 |
W. Chen, Y. Xu, and X. Wu, "Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling," arXiv:1711.07440 [cs.DC], pp. 1-2, 2017. [Online]. Available: http://arxiv.org/abs/1711.07440.
|
34 |
S. Hochreiter and J. Urgen Schmidhuber, "Long Short-term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
DOI
|
35 |
Q. Jing, H. Wang, and L. Yang, "Study on fast-changing mixed-modulation recognition based on neural network algorithms," KSII Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4664-4681, 2020.
DOI
|