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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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).
    2. Zhang, Yuwei & Li, Zhenping & Zhao, Yuwei, 2023. "Multi-mitigation strategies in medical supplies for epidemic outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    3. 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. M. Rezaei Kallaj & M. Hasannia Kolaee & S. M. J. Mirzapour Al-e-hashem, 2023. "Integrating bloodmobiles and drones in a post-disaster blood collection problem considering blood groups," Annals of Operations Research, Springer, vol. 321(1), pages 783-811, February.
    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. Radek Hrebik & Jaromir Kukal & Josef Jablonsky, 2019. "Optimal unions of hidden classes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 161-177, March.
    5. Moon, Kyungduk & Lee, Kangbok & Chopra, Sunil & Kwon, Steve, 2022. "Bilevel integer programming on a Boolean network for discovering critical genetic alterations in cancer development and therapy," European Journal of Operational Research, Elsevier, vol. 300(2), pages 743-754.
    6. 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.
    7. 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.
    8. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    9. Arshad Ali & Yuvraj Gajpal & Tarek Y. Elmekkawy, 2021. "Distributed permutation flowshop scheduling problem with total completion time objective," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 425-447, June.
    10. Phoebe Koundouri & Georgios I. Papayiannis & Electra V. Petracou & Athanasios N. Yannacopoulos, 2023. "Consensus group decision making under model uncertainty with a view towards environmental policy making," Papers 2312.00436, arXiv.org.
    11. Alparslan Gök, S.Z. & Özcan, İ., 2023. "On big boss fuzzy interval games," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1040-1046.
    12. 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.
    13. Luyao Wang & Hong Fan & Tianren Gong, 2018. "The Consumer Demand Estimating and Purchasing Strategies Optimizing of FMCG Retailers Based on Geographic Methods," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    14. 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.
    15. 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.
    16. Kanokporn Kungwalsong & Abraham Mendoza & Vasanth Kamath & Subramanian Pazhani & Jose Antonio Marmolejo-Saucedo, 2022. "An application of interactive fuzzy optimization model for redesigning supply chain for resilience," Annals of Operations Research, Springer, vol. 315(2), pages 1803-1839, August.
    17. 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.
    18. Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2023. "A flexible robust model for blood supply chain network design problem," Annals of Operations Research, Springer, vol. 328(1), pages 701-726, September.
    19. 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.
    20. Tirkolaee, Erfan Babaee & Golpîra, Hêriş & Javanmardan, Ahvan & Maihami, Reza, 2023. "A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

    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.