請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92127
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳政鴻 | zh_TW |
dc.contributor.advisor | Cheng-Hung Wu | en |
dc.contributor.author | 王文謙 | zh_TW |
dc.contributor.author | Wen-Chian Wang | en |
dc.date.accessioned | 2024-03-07T16:11:24Z | - |
dc.date.available | 2024-03-08 | - |
dc.date.copyright | 2024-03-07 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-02-18 | - |
dc.identifier.citation | 朱婉琳(2019). 利用人工智慧之消費者偏好學習與動態訂價方法. 臺灣大學工業工程學研究所學位論文.
Yang, Yang, Wan-Ling Chu, and Cheng-Hung Wu. "Learning customer preferences and dynamic pricing for perishable products." Computers & Industrial Engineering 171 (2022): 108440. den Boer, Arnoud V., and Bert Zwart. "Dynamic pricing and learning with finite inventories." Operations research 63.4 (2015): 965-978. Lin, Kyle Y. "Dynamic pricing with real-time demand learning." European Journal of Operational Research 174.1 (2006): 522-538. Gallego, Guillermo, and Garrett Van Ryzin. "Optimal dynamic pricing of inventories with stochastic demand over finite horizons." Management science 40.8 (1994): 999-1020. KINCAID, WM, and DA DARLING. An inventory pricing problem. MICHIGAN UNIV ANN ARBOR, 1962. Popescu, Ioana, and Yaozhong Wu. "Dynamic pricing strategies with reference effects." Operations research 55.3 (2007): 413-429. Caro, Felipe, and Jérémie Gallien. "Dynamic assortment with demand learning for seasonal consumer goods." Management science 53.2 (2007): 276-292. Bellman, Richard. "The theory of dynamic programming." Bulletin of the American Mathematical Society 60.6 (1954): 503-515. Li, Duan, et al. "Mitigation of curse of dimensionality in dynamic programming." IFAC Proceedings Volumes 41.2 (2008): 7778-7783. Ghose, Tapu Kumar, and Thomas T. Tran. "Dynamic pricing in electronic commerce using neural network." E-Technologies: Innovation in an Open World: 4th International Conference, MCETECH 2009, Ottawa, Canada, May 4-6, 2009. Proceedings 4. Springer Berlin Heidelberg, 2009. Ghose, Tapu Kumar, and Thomas T. Tran. "A dynamic pricing approach in e-commerce based on multiple purchase attributes." Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31–June 2, 2010. Proceedings 23. Springer Berlin Heidelberg, 2010. AmalNick, M., and Roozbeh Qorbanian. "Dynamic pricing using wavelet neural network under uncertain demands." Decision Science Letters 6.3 (2017): 251-260. Kong, Danxia. "One dynamic pricing strategy in agent economy using neural network based on online learning." IEEE/WIC/ACM International Conference on Web Intelligence (WI''04). IEEE, 2004. Genser, Alexander, and Anastasios Kouvelas. "Dynamic optimal congestion pricing in multi-region urban networks by application of a Multi-Layer-Neural network." Transportation Research Part C: Emerging Technologies 134 (2022): 103485. Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. "Learning representations by back-propagating errors." nature 323.6088 (1986): 533-536. Schäfer, Anton Maximilian, and Hans Georg Zimmermann. "Recurrent neural networks are universal approximators." Artificial Neural Networks–ICANN 2006: 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part I 16. Springer Berlin Heidelberg, 2006. Bengio, Yoshua, Patrice Simard, and Paolo Frasconi. "Learning long-term dependencies with gradient descent is difficult." IEEE transactions on neural networks 5.2 (1994): 157-166. Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. "On the difficulty of training recurrent neural networks." International conference on machine learning. Pmlr, 2013. Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780. Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling." arXiv preprint arXiv:1412.3555 (2014). Nelson, David MQ, Adriano CM Pereira, and Renato A. De Oliveira. "Stock market''s price movement prediction with LSTM neural networks." 2017 International joint conference on neural networks (IJCNN). Ieee, 2017. Sun, Yonghui, et al. "Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution." Wind Energy 23.1 (2020): 63-76. Li, Qingliang, et al. "GANs-LSTM model for soil temperature estimation from meteorological: a new approach." IEEE Access 8 (2020): 59427-59443. Yu, Shuyang, et al. "A domain adaptive convolutional LSTM model for prognostic remaining useful life estimation under variant conditions." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019. Sundermeyer, Martin, Hermann Ney, and Ralf Schlüter. "From feedforward to recurrent LSTM neural networks for language modeling." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23.3 (2015): 517-529. DeShon, Richard P. "A generalizability theory perspective on measurement error corrections in validity generalization." Validity generalization: A critical review (2003): 365-402. Lipton, Zachary C., et al. "Learning to diagnose with LSTM recurrent neural networks." arXiv preprint arXiv:1511.03677 (2015). Ji, Hongquan, Xiao He, and Donghua Zhou. "Diagnosis of sensor precision degradation using Kullback‐Leibler divergence." The Canadian Journal of Chemical Engineering 96.2 (2018): 434-443. Delpha, Claude, Demba Diallo, and Abdulrahman Youssef. "Kullback-Leibler Divergence for fault estimation and isolation: Application to Gamma distributed data." Mechanical Systems and Signal Processing 93 (2017): 118-135. Kullback, Solomon, and Richard A. Leibler. "On information and sufficiency." The annals of mathematical statistics 22.1 (1951): 79-86. Rust, John. "Has dynamic programming improved decision making?." Annual Review of Economics 11 (2019): 833-858. Escobari, Diego. "Dynamic pricing, advance sales and aggregate demand learning in airlines." The Journal of Industrial Economics 60.4 (2012): 697-724. Zhao, Wen, and Yu-Sheng Zheng. "Optimal dynamic pricing for perishable assets with nonhomogeneous demand." Management science 46.3 (2000): 375-388. Shewalkar, Apeksha, Deepika Nyavanandi, and Simone A. Ludwig. "Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU." Journal of Artificial Intelligence and Soft Computing Research 9.4 (2019): 235-245. Cahuantzi, Roberto, Xinye Chen, and Stefan Güttel. "A comparison of LSTM and GRU networks for learning symbolic sequences." Science and Information Conference. Cham: Springer Nature Switzerland, 2023. Astawa, I. Nyoman Gede Arya, I. Putu Bagus Arya Pradnyana, and I. Ketut Suwintana. "Comparison of RNN, LSTM, and GRU Methods on Forecasting Website Visitors." Journal of Computer Science and Technology Studies 4.2 (2022): 11-18. Mahjoub, Sameh, et al. "Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks." Sensors 22.11 (2022): 4062. Liu, Zhaoyang, et al. "Context-aware attention LSTM network for flood prediction." 2018 24th international conference on pattern recognition (ICPR). IEEE, 2018. Kutschinski, Erich, Thomas Uthmann, and Daniel Polani. "Learning competitive pricing strategies by multi-agent reinforcement learning." Journal of Economic Dynamics and Control 27.11-12 (2003): 2207-2218. Mullen, Patrick B., et al. "Particle swarm optimization in dynamic pricing." 2006 IEEE International Conference on Evolutionary Computation. IEEE, 2006. Lu, Renzhi, Seung Ho Hong, and Xiongfeng Zhang. "A dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach." Applied energy 220 (2018): 220-230. Maestre, Roberto, et al. "Reinforcement learning for fair dynamic pricing." Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. Springer International Publishing, 2019. Schwind, Michael, and Oliver Wendt. "Dynamic pricing of information products based on reinforcement learning: A yield-management approach." Annual Conference on Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. Su, Xuanming. "Intertemporal pricing with strategic customer behavior." Management Science 53.5 (2007): 726-741. Aviv, Yossi, and Amit Pazgal. "Optimal pricing of seasonal products in the presence of forward-looking consumers." Manufacturing & service operations management 10.3 (2008): 339-359. Chade, Hector, and Virginia Vera De Serio. "Pricing, learning, and strategic behavior in a single-sale model." Economic Theory 19 (2002): 333-353. Mersereau, Adam J., and Dan Zhang. "Markdown pricing with unknown fraction of strategic customers." Manufacturing & Service Operations Management 14.3 (2012): 355-370. Kachani, Soulaymane, Georgia Perakis, and Carine Simon. "Modeling the transient nature of dynamic pricing with demand learning in a competitive environment." Network science, nonlinear science and infrastructure systems (2007): 223-267. Priester, Anna, Thomas Robbert, and Stefan Roth. "A special price just for you: Effects of personalized dynamic pricing on consumer fairness perceptions." Journal of Revenue and Pricing Management 19 (2020): 99-112. Shakya, Siddhartha, Fernando Oliveira, and Gilbert Owusu. "Analysing the effect of demand uncertainty in dynamic pricing with EAs." International Conference on Innovative Techniques and Applications of Artificial Intelligence. London: Springer London, 2008. Shakya, Siddhartha, et al. "Neural network demand models and evolutionary optimisers for dynamic pricing." Knowledge-Based Systems 29 (2012): 44-53. Teodorović, Dušan, and Praveen Edara. "A real-time road pricing system: The case of a two-link parallel network." Computers & Operations Research 34.1 (2007): 2-27. Cournot, Antoine Augustin. Researches into the Mathematical Principles of the Theory of Wealth. New York: Macmillan Company, 1927 [c1897], 1927. Mills, Edwin S. "Uncertainty and price theory." The Quarterly Journal of Economics 73.1 (1959): 116-130. Nevins, Arthur J. "Some effects of uncertainty: Simulation of a model of price." The Quarterly Journal of Economics 80.1 (1966): 73-87. Sandmo, Agnar. "On the theory of the competitive firm under price uncertainty." The American Economic Review 61.1 (1971): 65-73. Aoki, Masanao. "On a dual control approach to the pricing policies of a trading specialist." IFIP Technical Conference on Optimization Techniques. Berlin, Heidelberg: Springer Berlin Heidelberg, 1973. Qu, Huashuai, Ilya O. Ryzhov, and Michael C. Fu. "Learning logistic demand curves in business-to-business pricing." 2013 Winter Simulations Conference (WSC). IEEE, 2013. Wruck, Eric Gordon. Dynamic pricing implications of uncertainty about demand. Cornell University, 1989. Venezia, Itzhak. "Optimal investments in market research." European Journal of Operational Research 18.2 (1984): 198-207. Morales-Enciso, Sergio, and Jürgen Branke. "Revenue maximization through dynamic pricing under unknown market behaviour." 3rd Student Conference on Operational Research. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2012. Carvalho, Alexandre Xavier Ywata de, and Martin L. Puterman. "Dynamic optimization and learning: How should a manager set prices when the demand function is unknown?." (2005). Eren, Serkan S., and Costis Maglaras. "Monopoly pricing with limited demand information." Journal of revenue and pricing management 9 (2010): 23-48. Farias, Vivek F., and Benjamin Van Roy. "Dynamic pricing with a prior on market response." Operations Research 58.1 (2010): 16-29. Avramidis, Athanassios. "Learning in revenue management: exploiting estimation of arrival rate and price response." Pre-print (2011). Chen, Yongmin, and Ruqu Wang. "Learning buyers'' valuation distribution in posted-price selling." Economic Theory 14 (1999): 417-428. Aviv, Yossi, and Amit Pazgal. "A partially observed Markov decision process for dynamic pricing." Management science 51.9 (2005): 1400-1416. Xiong, Yu, Gendao Li, and Kiran Jude Fernandes. "Dynamic pricing model and algorithm for perishable products with fuzzy demand." Applied Stochastic Models in Business and Industry 26.6 (2010): 758-774. Abdella, Juhar Ahmed, et al. "Airline ticket price and demand prediction: A survey." Journal of King Saud University-Computer and Information Sciences 33.4 (2021): 375-391. Hole, Arne Risa, and Julie Riise Kolstad. "Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment." Empirical Economics 42 (2012): 445-469. Chen, Shiyu, et al. "Dynamic pricing for smart mobile edge computing: A reinforcement learning approach." IEEE Wireless Communications Letters 10.4 (2020): 700-704. Wang, Yining, et al. "Uncertainty quantification for demand prediction in contextual dynamic pricing." Production and Operations Management 30.6 (2021): 1703-1717. Keskin, N. Bora, Yuexing Li, and Jing-Sheng Song. "Data-driven dynamic pricing and ordering with perishable inventory in a changing environment." Management Science 68.3 (2022): 1938-1958. Asghari, Mohammad, and Cyrus Shahabi. "An on-line truthful and individually rational pricing mechanism for ride-sharing." Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2017. Asghari, Mohammad. Dynamic Pricing and Task Assignment in Real-Time Spatial Crowdsourcing Platforms. Diss. University of Southern California, 2018. Javanmard, Adel, Hamid Nazerzadeh, and Simeng Shao. "Multi-product dynamic pricing in high-dimensions with heterogeneous price sensitivity." 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. Li, Yanbin, et al. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning." Energy 281 (2023): 128284. Xiao, Chunjing, et al. "Modeling Behavior and Attribute feedback based Flight Recommendation for Dynamic Pricing." 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). IEEE, 2022. Wen, Chieh-Hua, and Po-Hung Chen. "Passenger booking timing for low-cost airlines: A continuous logit approach." Journal of Air Transport Management 64 (2017): 91-99. Escobari, Diego. "Estimating dynamic demand for airlines." Economics Letters 124.1 (2014): 26-29. An, Bo, et al. "Data-driven frequency-based airline profit maximization." ACM Transactions on Intelligent Systems and Technology (TIST) 8.4 (2017): 1-28. Yuan, Hui, Wei Xu, and Chengfu Yang. "A user behavior-based ticket sales prediction using data mining tools: An empirical study in an OTA company." 2014 11th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2014. Mostafaeipour, Ali, Alireza Goli, and Mojtaba Qolipour. "Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study." The journal of supercomputing 74 (2018): 5461-5484. Pan, Boxiao, et al. "A novel LSTM-based daily airline demand forecasting method using vertical and horizontal time series." Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers 22. Springer International Publishing, 2018. Kumar, Avinash, Sobhangi Sarkar, and Chittaranjan Pradhan. "Malaria disease detection using cnn technique with sgd, rmsprop and adam optimizers." Deep learning techniques for biomedical and health informatics (2020): 211-230. Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15.1 (2014): 1929-1958. Nwankpa, Chigozie, et al. "Activation functions: Comparison of trends in practice and research for deep learning." arXiv preprint arXiv:1811.03378 (2018). Chen, Yuwen, et al. "An ensemble learning based approach for building airfare forecast service." 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92127 | - |
dc.description.abstract | 需求學習(Demand Learning)可以幫助供應商了解消費者喜好來達到滿足市場需求,但往往受限於歷史資料不足及環境變動快速等問題,無法得到最佳的效果。因應此問題,朱婉琳(2019)提出一種結合動態規劃(Dynamic Programming)與深度學習(Deep Learning)之技術應用在動態訂價(Dynamic Pricing)與需求學習中,該模型使用長短期記憶神經網路(Long Short-Term Memory neuron network, LSTM)對各種市場環境進行消費者需求學習,在面對新環境時透過歷史資料估計新環境下消費者支付意願(Willing to Pay, WTP)之參數,透過動態訂價求解出最佳決策並逼近最佳利潤。
然而,朱婉琳之研究僅透過神經網路學習消費者支付意願分布之平均值,並假設支付意願服從標準差為固定值之常態分布(Normal Distribution),並不符合實際市場中的狀況:消費者支付意願為服從任意常態分布,即常態分布之平均值及標準差值皆是變動的,亦或是支付意願服從其他各種不同分布。因此,本研究將使用神經網路模型學習消費者支付意願分布,其中透過KL Divergence 作為神經網路的損失函數(Loss)去學習常態分布之平均值及標準差,或是學習伽瑪分布的參數,更符合實際市場銷售情形,並透過動態訂價求解,以達到供應商販售時效性商品之最大化利潤。 最後透過模擬訂價驗證在各式各樣的市場環境下,本研究提出的模型僅需要用很少的成本學習消費者的需求,且模擬訂價的結果與動態規劃求解最佳利潤的差異很少,結果證實,本研究之模型能在未知的環境下擁有良好的能力。 | zh_TW |
dc.description.abstract | Demand Learning can help suppliers understand consumer preferences to meet market demand. However, it is often limited by insufficient historical data and rapid environmental changes, and cannot achieve the best results. In response to this problem, Zhu(2019) proposed a technology that combines dynamic programming and deep learning to be used in dynamic pricing and demand learning. This model uses Long Short-Term Memory neuron network(LSTM) conducts consumer demand learning in various market environments, and uses historical data to predict consumer willingness to pay (WTP) in the new environment through historical data. Dynamic pricing solves the best decision and approaches the best profit.
However, Zhu''s research only used neural networks to learn the average value of consumers'' WTP distribution, and assumed that WTP obeys a normal distribution with a fixed standard deviation, which is not consistent with the situation in the actual market: consumers The WTP is subject to any normal distribution, that is, the mean and standard deviation of the normal distribution are changing, or the WTP is subject to various other different distributions. Therefore, this study will use a neural network model to learn the distribution of consumers'' willingness to pay, using KL Divergence as the loss function of the neural network to learn the mean and standard deviation of the normal distribution, or learn the gamma distribution The parameters are more in line with the actual market sales situation and are solved through dynamic pricing to maximize the supplier''s profit from selling time-sensitive goods. Finally, it is verified through simulated pricing that in various market environments, the model proposed in this study only needs to learn consumer needs at a very small cost, and the results of simulated pricing are different from those of dynamic programming to solve the optimal profit. Very rarely, the results confirm that the model in this study has good capabilities in unknown environments. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-07T16:11:24Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-03-07T16:11:24Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要及關鍵詞 ii 英文摘要及關鍵詞 iii 目次 v 圖次 vii 表次 x 第一章 緒論 1 1.1研究背景與動機 1 1.2研究目的 2 1.3研究方法與流程 2 第二章 文獻回顧 4 2.1動態訂價 4 2.2人工智慧在動態訂價之應用 8 2.2.1機器學習在動態訂價之應用 8 2.2.2 神經網路在動態訂價之應用 9 2.2.3 時間序列預測之神經網路 9 2.2.4 損失函數的選擇 11 2.3 小結 12 第三章 長短期記憶神經網路之架構 14 3.1長短期記憶神經網路之介紹 14 3.2消費者購買資料蒐集 16 3.2.1 消費者購買資料模擬 16 3.2.2 資料特徵萃取 18 3.2.3 模擬資料劃分 20 3.3神經網路架構 20 第四章 動態訂價模型 25 4.1模型基本架構與假設 25 4.2動態訂價模型 26 第五章 模型測試與模擬驗證 30 5.1 WTP服從常態分布 30 5.1.1 參數設置 30 5.1.2 長短期記憶神經網路模型測試 31 5.1.3 模擬驗證流程 33 5.1.4 模擬驗證結果 36 5.2 WTP服從伽瑪分布 47 5.2.1 參數設置 47 5.2.2 長短期記憶神經網路模型測試 48 5.2.3 模擬驗證流程 50 5.2.4 模擬驗證結果 51 第六章 結論與未來方向 66 6.1 結論 66 6.2 未來研究方向 66 參考文獻 68 | - |
dc.language.iso | zh_TW | - |
dc.title | 長短期記憶神經網路之消費者偏好學習與動態訂價運用 | zh_TW |
dc.title | Adopting Long Short-Term Memory Neural Network for Consumer Preference Learning and Dynamic Pricing | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 陳文智;周育樂 | zh_TW |
dc.contributor.oralexamcommittee | Wen-Chih Chen;Ywh-Leh Chou | en |
dc.subject.keyword | 動態規劃,動態訂價,需求學習,深度學習,長短期記憶神經網路, | zh_TW |
dc.subject.keyword | Dynamic programming,Dynamic pricing,Demand learning,Deep learning,LSTM neural network, | en |
dc.relation.page | 77 | - |
dc.identifier.doi | 10.6342/NTU202400728 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2024-02-18 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工業工程學研究所 | - |
顯示於系所單位: | 工業工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-112-1.pdf | 4.03 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。