IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v312y2022i2d10.1007_s10479-020-03871-7.html
   My bibliography  Save this article

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Author

Listed:
  • Soheyl Khalilpourazari

    (Concordia University
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Hossein Hashemi Doulabi

    (Concordia University
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

Abstract

World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method’s efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E−06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

Suggested Citation

  • Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2022. "Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec," Annals of Operations Research, Springer, vol. 312(2), pages 1261-1305, May.
    • Handle: RePEc:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-020-03871-7
    DOI: 10.1007/s10479-020-03871-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03871-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03871-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Sheng-yao & Wang, Ling & Liu, Min & Xu, Ye, 2013. "An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 387-396.
    2. Kamal Z Zamli & Fakhrud Din & Bestoun S Ahmed & Miroslav Bures, 2018. "A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-29, May.
    3. Weber, Gerhard-Wilhelm & Defterli, Ozlem & Alparslan Gök, SIrma Zeynep & Kropat, Erik, 2011. "Modeling, inference and optimization of regulatory networks based on time series data," European Journal of Operational Research, Elsevier, vol. 211(1), pages 1-14, May.
    4. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    5. Hossein Hashemi Doulabi & Gilles Pesant & Louis-Martin Rousseau, 2020. "Vehicle Routing Problems with Synchronized Visits and Stochastic Travel and Service Times: Applications in Healthcare," Transportation Science, INFORMS, vol. 54(4), pages 1053-1072, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maleki, Abolfazl & Hemmati, Vahid & Reza Abazari, Seyed & Aghsami, Amir & Rabbani, Masoud, 2024. "Optimal distribution and waste management of Covid-19 vaccines from vaccination centers’ satisfaction perspective – A fuzzy time window-based VRP," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. Colajanni, Gabriella & Daniele, Patrizia & Sciacca, Daniele, 2022. "Reagents and swab tests during the COVID-19 Pandemic: An optimized supply chain management with UAVs," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Zhang, Yuwei & Li, Zhenping & Zhao, Yuwei, 2023. "Multi-mitigation strategies in medical supplies for epidemic outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    4. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    2. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    3. Yan Li & Xiao Xu & Fuyu Wang, 2023. "Research on Home Health Care Scheduling Considering Synchronous Access of Caregivers and Vehicles," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    4. Soheyl Khalilpourazari & Seyed Hamid Reza Pasandideh, 2021. "Designing emergency flood evacuation plans using robust optimization and artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 41(3), pages 640-677, April.
    5. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    6. Soares, Ricardo & Marques, Alexandra & Amorim, Pedro & Parragh, Sophie N., 2024. "Synchronisation in vehicle routing: Classification schema, modelling framework and literature review," European Journal of Operational Research, Elsevier, vol. 313(3), pages 817-840.
    7. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    8. Zomorrodi, Ali R. & Maranas, Costas D., 2014. "Coarse-grained optimization-driven design and piecewise linear modeling of synthetic genetic circuits," European Journal of Operational Research, Elsevier, vol. 237(2), pages 665-676.
    9. Hatami, Sara & Ruiz, Rubén & Andrés-Romano, Carlos, 2015. "Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times," International Journal of Production Economics, Elsevier, vol. 169(C), pages 76-88.
    10. Xiaohui Zhang & Xinhua Liu & Shufeng Tang & Grzegorz Królczyk & Zhixiong Li, 2019. "Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 12(17), pages 1-24, August.
    11. Xiong, Fuli & Xing, Keyi & Wang, Feng, 2015. "Scheduling a hybrid assembly-differentiation flowshop to minimize total flow time," European Journal of Operational Research, Elsevier, vol. 240(2), pages 338-354.
    12. Shichang Xiao & Shudong Sun & Jionghua (Judy) Jin, 2017. "Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems," Energies, MDPI, vol. 10(4), pages 1-26, April.
    13. Taheri, Nima & Jahani, Hamed & Pishvaee, Mir Saman, 2024. "Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach," Renewable Energy, Elsevier, vol. 227(C).
    14. M. T. Agieva & A. V. Korolev & G. A. Ougolnitsky, 2020. "Modeling and Simulation of Impact and Control in Social Networks with Application to Marketing," Mathematics, MDPI, vol. 8(9), pages 1-28, September.
    15. Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
    16. Maryam Daryalal & Hamed Pouya & Marc Antoine DeSantis, 2023. "Network Migration Problem: A Hybrid Logic-Based Benders Decomposition Approach," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 593-613, May.
    17. Turgut, Oguz Emrah & Turgut, Mert Sinan, 2023. "Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 302-374.
    18. Abdorrrahman Haeri & Seyyed-Mahdi Hosseini-Motlagh & Mohammad Reza Ghatreh Samani & Marziehsadat Rezaei, 2022. "An integrated socially responsible-efficient approach toward health service network design," Annals of Operations Research, Springer, vol. 319(1), pages 463-516, December.
    19. Li, Yanfeng & Xiang, Ting & Szeto, Wai Yuen, 2021. "Home health care routing and scheduling problem with the consideration of outpatient services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    20. Bahman Naderi & Vahid Roshanaei & Mehmet A. Begen & Dionne M. Aleman & David R. Urbach, 2021. "Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2608-2635, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-020-03871-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.