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Research Article
Virtual Queue for Public Distribution System Using Deep Q Learning Based Slot Prediction
CHINNADURAI S1
OLIVIYA T2
THAGAMALAR D3
NISHANTHINI S4
SUREKA K5
12345 Dhanalakshmi Srinivasan Engineering college, Perambalur / Anna University, India.
Published Online: March-April 2022
Pages: 83-86
Cite this article
No DOIReferences
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ITM Web Conf, vol. 30, pp. 04014, 2019.
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heterogeneous multiprocessor platforms,’’ in Proc. IEEE 40th Real-Time Syst. Symp. (RTSS), Dec. 2019, pp. 540–543, doi:
10.1109/RTSS46320.2019.00059.
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vol. 90, pp. 72–84, Oct. 2018, doi: 10.1016/j.sysarc.2018.09.002.
5. W. Bouazza, Y. Sallez, and B. Beldjilali, ‘‘A distributed approach solving partially flexible job-shop scheduling problem with a Qlearning effect,’’ IFAC-PapersOnLine, vol. 50, no. 1, pp. 15890–15895, Jul. 2017.
6. Y.-R. Shiue, K.-C. Lee, and C.-T. Su, ‘‘Real-time scheduling for a smart factory using a reinforcement learning approach,’’ Comput. Ind.
Eng., vol. 125, pp. 604–614, Nov. 2018.
7. J. Shahrabi, M. A. Adibi, and M. Mahootchi, ‘‘A reinforcement learning approach to parameter estimation in dynamic job shop
scheduling,’’ Comput. Ind. Eng., vol. 110, pp. 75–82, Aug. 2017, doi: 10.1016/j.cie.2017.05.026.
8. Y.-F. Wang, ‘‘Adaptive job shop scheduling strategy based on weighted Q-learning algorithm,’’ J. Intell. Manuf., vol. 31, no. 2, pp. 417–
432, Feb. 2020, doi: 10.1007/s10845-018-1454-3.
9. C. Mogilner, H. E. Hershfield, and J. Aaker, “Rethinking time: Implications for well-being,” Consumer Psychology Review, vol. 1, no.
1, pp. 41–53, 2018.
10. S. U¨ lku¨, C. Hydock, and S. Cui, “Making the wait worthwhile: Experiments on the effect of queueing on consumption,” Management
Science, 2019.
11. J. F. Shortle, J. M. Thompson, D. Gross, and C. M. Harris, Fundamentals of queueing theory. John Wiley & Sons, 2018, vol. 399.
12. A. Joseph, T. Hijal, J. Kildea, L. Hendren, and D. Herrera, “Predicting waiting times in radiation oncology using machine learning,”
in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017, pp. 1024–1029.
13. C. Curtis, C. Liu, T. J. Bollerman, and O. S. Pianykh, “Machine learning for predicting patient wait times and appointment delays,”
Journal of the American College of Radiology, vol. 15, no. 9, pp. 1310–1316, 2018.
14. S. A. Bishop, H. I. Okagbue, P. E. Oguntunde, A. A. Opanuga, and O. Odetunmibi, “Survey dataset on analysis of queues in some selected
banks in ogun state, nigeria,” Data in brief, vol. 19, pp. 835–841, 2018.
15. A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375, 2018.
Single Phase", vol. 3, no. 4, 2020.
2. N. Andriyanov and V. Sonin, "The use of random process models and machine learning to analyze the operation of a taxi order service",
ITM Web Conf, vol. 30, pp. 04014, 2019.
3. J. Chen, C. Du, P. Han, and X. Du, ‘‘Work-in-progress: non-preemptive scheduling of periodic tasks with data dependency upon
heterogeneous multiprocessor platforms,’’ in Proc. IEEE 40th Real-Time Syst. Symp. (RTSS), Dec. 2019, pp. 540–543, doi:
10.1109/RTSS46320.2019.00059.
4. J. Chen, C. Du, F. Xie, and B. Lin, ‘‘Scheduling non-preemptive tasks with strict periods in multi-core real-time systems,’’ J. Syst. Archit.,
vol. 90, pp. 72–84, Oct. 2018, doi: 10.1016/j.sysarc.2018.09.002.
5. W. Bouazza, Y. Sallez, and B. Beldjilali, ‘‘A distributed approach solving partially flexible job-shop scheduling problem with a Qlearning effect,’’ IFAC-PapersOnLine, vol. 50, no. 1, pp. 15890–15895, Jul. 2017.
6. Y.-R. Shiue, K.-C. Lee, and C.-T. Su, ‘‘Real-time scheduling for a smart factory using a reinforcement learning approach,’’ Comput. Ind.
Eng., vol. 125, pp. 604–614, Nov. 2018.
7. J. Shahrabi, M. A. Adibi, and M. Mahootchi, ‘‘A reinforcement learning approach to parameter estimation in dynamic job shop
scheduling,’’ Comput. Ind. Eng., vol. 110, pp. 75–82, Aug. 2017, doi: 10.1016/j.cie.2017.05.026.
8. Y.-F. Wang, ‘‘Adaptive job shop scheduling strategy based on weighted Q-learning algorithm,’’ J. Intell. Manuf., vol. 31, no. 2, pp. 417–
432, Feb. 2020, doi: 10.1007/s10845-018-1454-3.
9. C. Mogilner, H. E. Hershfield, and J. Aaker, “Rethinking time: Implications for well-being,” Consumer Psychology Review, vol. 1, no.
1, pp. 41–53, 2018.
10. S. U¨ lku¨, C. Hydock, and S. Cui, “Making the wait worthwhile: Experiments on the effect of queueing on consumption,” Management
Science, 2019.
11. J. F. Shortle, J. M. Thompson, D. Gross, and C. M. Harris, Fundamentals of queueing theory. John Wiley & Sons, 2018, vol. 399.
12. A. Joseph, T. Hijal, J. Kildea, L. Hendren, and D. Herrera, “Predicting waiting times in radiation oncology using machine learning,”
in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017, pp. 1024–1029.
13. C. Curtis, C. Liu, T. J. Bollerman, and O. S. Pianykh, “Machine learning for predicting patient wait times and appointment delays,”
Journal of the American College of Radiology, vol. 15, no. 9, pp. 1310–1316, 2018.
14. S. A. Bishop, H. I. Okagbue, P. E. Oguntunde, A. A. Opanuga, and O. Odetunmibi, “Survey dataset on analysis of queues in some selected
banks in ogun state, nigeria,” Data in brief, vol. 19, pp. 835–841, 2018.
15. A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375, 2018.
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