A reinforcement learning based dynamic room pricing model for hotel industry

References

• Ban GY, Keskin NB. 2021. Personalized dynamic pricing with machine learning: high-dimensional features and heterogeneous elasticity. Manage Sci. 67(9):5549–5568. doi:10.1287/mnsc.2020.3680.
   Web of Science ®Google Scholar
• Chai D, Wu W, Han Q, Wu F, Li J. 2020. Description based text classification with reinforcement learning. In International Conference on Machine Learning, PMLR, p. 1371–1382.
   Google Scholar
• Ferrara M. 2018. [A reinforcement learning approach to dynamic pricing]. [dissertation]. Politecnico di Torino.
   Google Scholar
• Ferreira KJ, Lee BHA, Simchi-Levi D. 2016. Analytics for an online retailer: demand forecasting and price optimization. M&SOM. 18(1):69–88. doi:10.1287/msom.2015.0561.
   Web of Science ®Google Scholar
• Garcıa J, Fernández F. 2015. A comprehensive survey on safe reinforcement learning. J Mach Learn Res. 16(1):1437–1480.
   Google Scholar
• Ghalehkhondabi I, Ardjmand E, Young WA, Weckman GR. 2019. A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. JTF. 5(1):75–93. doi:10.1108/JTF-10-2018-0061.
   Web of Science ®Google Scholar
• Gibbs C, Guttentag D, Gretzel U, Yao L, Morton J. 2018. Use of dynamic pricing strategies by airbnb hosts. IJCHM. 30(1):2–20. doi:10.1108/IJCHM-09-2016-0540.
   Google Scholar
• Greenstein-Messica A, Rokach L. 2020. Machine learning and operation research based method for promotion optimization of products with no price elasticity history. Electron Commerc Res Appl. 40:100914. doi:10.1016/j.elerap.2019.100914.
   Web of Science ®Google Scholar
• He QQ, Wu C, Si YW. 2022. Lstm with particle swam optimization for sales forecasting. Electron Commerc Res Appl. 51:101118. doi:10.1016/j.elerap.2022.101118.
   Web of Science ®Google Scholar
• Javed HT, Beg MO, Mujtaba H, Majeed H, Asim M. 2019. Fairness in real-time energy pricing for smart grid using unsupervised learning. Comput J. 62(3):414–429. doi:10.1093/comjnl/bxy071.
   Web of Science ®Google Scholar
• Jiaqi Xu J, Fader PS, Veeraraghavan S. 2019. Designing and evaluating dynamic pricing policies for major league baseball tickets. M&SOM. 21(1):121–138. doi:10.1287/msom.2018.0760.
   Google Scholar
• Jin B, Cruz L, Gonçalves N. 2020. Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEE Access. 8:123649–123661. doi:10.1109/ACCESS.2020.3005687.
   Google Scholar
• Jin B, Cruz L, Gonçalves N. 2022. Pseudo rgb-d face recognition. IEEE Sensors J. 22(22):21780–21794. doi:10.1109/JSEN.2022.3197235.
   Web of Science ®Google Scholar
• Jun W, Jinzhou Z. 2022. Q-learning based radio resources allocation in cognitive satellite communication. In 2022 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, p. 1–5.
   Google Scholar
• Kim BG, Zhang Y, Van Der Schaar M, Lee JW. 2015. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans Smart Grid. 7(5):2187–2198. doi:10.1109/TSG.2015.2495145.
   Web of Science ®Google Scholar
• Law R, Li G, Fong DKC, Han X. 2019. Tourism demand forecasting: a deep learning approach. Ann Tourism Res. 75:410–423. doi:10.1016/j.annals.2019.01.014.
   Web of Science ®Google Scholar
• Li X, Law R, Xie G, Wang S. 2021. Review of tourism forecasting research with internet data. Tour Manage. 83:104245. doi:10.1016/j.tourman.2020.104245.
   Web of Science ®Google Scholar
• Liu J, Zhang Y, Wang X, Deng Y, Wu X, Xie M. 2018. Dynamic pricing on e-commerce platform with deep reinforcement learning. arXiv preprint arXiv:191202572.
   Google Scholar
• Lu R, Hong SH, Zhang X. 2018. A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach. Appl Energy. 220:220–230. doi:10.1016/j.apenergy.2018.03.072.
   Web of Science ®Google Scholar
• Maestre R, Duque J, Rubio A, Arévalo J. 2019. Reinforcement learning for fair dynamic pricing. In Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 (pp. 120–135). Springer International Publishing.
   Google Scholar
• Maoudj A, Hentout A. 2020. Optimal path planning approach based on q-learning algorithm for mobile robots. Appl Soft Comput. 97:106796. doi:10.1016/j.asoc.2020.106796.
   Web of Science ®Google Scholar
• Montazeri M, Kebriaei H, Araabi BN. 2020. Learning pareto optimal solution of a multi-attribute bilateral negotiation using deep reinforcement. Electron Commerc Res Appl. 43:100987. doi:10.1016/j.elerap.2020.100987.
   Web of Science ®Google Scholar
• Paiva BB, Nascimento ER, Gonçalves MA, Belém F. 2022. A reinforcement learning approach for single redundant view co-training text classification. Inf Sci. 615:24–38. doi:10.1016/j.ins.2022.09.065.
   Web of Science ®Google Scholar
• Sahu B, Das PK, Ranjan Kabat M. 2022. Multi-robot cooperation and path planning for stick transporting using improved q-learning and democratic robotics pso. J Comput Sci. 60:101637. doi:10.1016/j.jocs.2022.101637.
   Web of Science ®Google Scholar
• Song H, Qiu RT, Park J. 2019. A review of research on tourism demand forecasting: launching the annals of tourism research curated collection on tourism demand forecasting. Ann Tour Res. 75:338–362. doi:10.1016/j.annals.2018.12.001.
   Web of Science ®Google Scholar
• Sutton RS, Barto AG. 2018. Reinforcement learning: an introduction. MIT Press.
   Google Scholar
• Taleizadeh AA, Safaei AZ, Bhattacharya A, Amjadian A. 2022. Online peer-to-peer lending platform and supply chain finance decisions and strategies. Ann Oper Res. 315(1):397–427. doi:10.1007/s10479-022-04648-w.
   Web of Science ®Google Scholar
• Taleizadeh AA, Varzi AM, Amjadian A, Noori-Daryan M, Konstantaras I. 2023. How cash-back strategy affect sale rate under refund and customers’ credit. Oper Res Int J. 23(1):19. doi:10.1007/s12351-023-00752-2.
   Web of Science ®Google Scholar
• Vázquez-Canteli JR, Nagy Z. 2019. Reinforcement learning for demand response: a review of algorithms and modelling techniques. Appl Energy. 235:1072–1089. doi:10.1016/j.apenergy.2018.11.002.
   Web of Science ®Google Scholar
• Wang M, Deng W. 2020. Mitigating bias in face recognition using skewness-aware reinforcement learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. p. 9319–9328. doi:10.1109/CVPR42600.2020.00934.
   Google Scholar
• Ye P, Qian J, Chen J, Wu C, Zhou Y, De Mars S, Yang F, Zhang L. 2018. Customized regression model for airbnb dynamic pricing. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, p. 932–940. doi:10.1145/3219819.3219830.
   Google Scholar
• Zheng Q, Tian X, Yang M, Wu Y, Su H. 2020. Pac-bayesian framework based drop-path method for 2d discriminative convolutional network pruning. Multidim Syst Sign Process. 31(3):793–827. doi:10.1007/s11045-019-00686-z.
   Web of Science ®Google Scholar
• Zheng Q, Zhao P, Li Y, Wang H, Yang Y. 2021a. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Comput Appl. 33(13):7723–7745. doi:10.1007/s00521-020-05514-1.
   Web of Science ®Google Scholar
• Zheng Q, Zhao P, Zhang D, Wang H. 2021b. Mr-dcae: manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. Int J Intell Sys. 36(12):7204–7238. doi:10.1002/int.22586.
   Web of Science ®Google Scholar