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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政鴻 | zh_TW |
| dc.contributor.advisor | Cheng-Hung Wu | en |
| dc.contributor.author | 郭紫萱 | zh_TW |
| dc.contributor.author | Tzu-Hsuan Kuo | en |
| dc.date.accessioned | 2024-08-16T17:25:10Z | - |
| dc.date.available | 2024-08-31 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-05 | - |
| dc.identifier.citation | Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering, 143, 106435.
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Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. 2016 IEEE international conference on aircraft utility systems (AUS), Zha, Y., Zhang, L., Xu, C., & Zhang, T. (2021). A two‐period pricing model with intertemporal and horizontal reference price effects. International Transactions in Operational Research, 28(3), 1417-1440. Zhang, X., Yu, F. X., Chang, S.-F., & Wang, S. (2015). Deep transfer network: Unsupervised domain adaptation. arXiv preprint arXiv:1503.00591. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94667 | - |
| dc.description.abstract | 本研究將深度學習技術應用於需求參數估計與動態訂價，訓練出能在未知市場環境做精準估計並即時進行動態決策優化的模型。
考量參考價格的動態訂價決策對販售時效性商品的業者來說至關重要。市場環境中的消費者平均價值常難以量化且取得不易，若無法有效率地估計以掌握消費者需求輪廓，將影響訂價決策並造成獲利損失。本研究提出之方法與開發之模型將解決上述困境。 本研究探討環境為販售單一時效性商品的市場，目標是令預期利潤最大化——應用多項羅吉特模型(Multinomial Logit Model，MNL)將消費者平均價值與參考價格納入動態訂價狀態參數，求解所有狀態集合下的最佳定價策略，以達預期利潤最大化之目的；其次，使用大量歷史銷售資料作為訓練樣本，開發出可在未知市場環境中精準估計消費者平均價值之長短期記憶神經網路(Long Short-Term Momery，LSTM)；最後，藉由自適應訓練(Adaptive Training)逐步提升LSTM估計效能及訂價決策成效，令模型得利用真實銷售資料來適應當前市場環境，並透過離散事件模擬來驗證模型於未知環境中的估計準確度與通用性。實驗結果證實，本研究開發之模型在未知市場環境也具備良好效能，可供實務應用參考。 | zh_TW |
| dc.description.abstract | This research applies deep learning method in demand parameters estimation and dynamic pricing to train a model that can make accurate estimation in unknown market environments and conduct decision optimization in real time.
Good dynamic pricing strategy considering reference prices are crucial to sellers with short selling season. Mean of customers’ valuation are usually difficult to quantify and estimate. Being unable to estimate demand parameters efficiently will lead to bad pricing strategy and considerable profit loss. Our study proposes a model to solve the difficulties mentioned above. We consider a dynamic pricing problem in a market selling single perishable product with the goal of maximizing expected profits. We take mean of customers’ valuation and reference prices into the consideration of dynamic pricing with the application of multinomial logit model, solving the optimal pricing strategies under all possible states. Then we use a large amount of historical sales data to develop a long short-term memory neural network which can precisely estimate mean of customers’ valuation in unknown market environments. Finally, we apply adaptive training to improve the performance of LSTM’s estimation and pricing strategy, let model be self-adaptive to current market environments by utilizing real-time sales data, then use discrete events simulation to verify our model’s accuracy and generalization under unknown environments. The result indicates that our model performs well in a variety of unknown market environments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T17:25:10Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T17:25:10Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 ........................................................................................................... i
致謝 .................................................................................................................................. ii 中文摘要 ......................................................................................................................... iii ABSTRACT .................................................................................................................... iv 目次 .................................................................................................................................. v 圖次 ................................................................................................................................ vii 表次 ................................................................................................................................. ix 第一章 前言 .............................................................................................................. 1 1.1 研究背景與動機 .......................................................................................... 1 1.1.1 動態訂價 .......................................................................................... 1 1.1.2 參考價格 .......................................................................................... 1 1.1.3 需求參數估計 .................................................................................. 2 1.1.4 研究動機及小結 .............................................................................. 2 1.2 研究目的 ...................................................................................................... 3 1.3 研究方法與流程 .......................................................................................... 3 第二章 文獻探討 ...................................................................................................... 6 2.1 動態訂價 ...................................................................................................... 6 2.2 參考價格 ...................................................................................................... 7 2.3 需求參數估計 .............................................................................................. 8 2.4 考量參考價格的動態訂價 .......................................................................... 9 2.5 自適應訓練 .................................................................................................. 9 2.6 小結 ............................................................................................................ 10 第三章 考量參考價格且參數已知的動態訂價模型 ............................................ 11 3.1 問題架構與相關假設 ................................................................................ 11 3.2 動態規劃模型 ............................................................................................ 12 3.2.1 考量參考價格之購買機率計算——多項羅吉特模型應用 ........ 13 3.2.2 動態規劃模型 ................................................................................ 15 3.3 動態規劃程式 ............................................................................................ 17 3.4 參數設定與模型測試 ................................................................................ 18 3.5 可改善方向 ................................................................................................ 23 第四章 考量參考價格但參數未知的動態訂價模型 ............................................ 24 4.1 問題架構與相關假設 ................................................................................ 24 4.2 動態規劃模型 ............................................................................................ 26 4.2.1 需求參數估計——長短期記憶神經網路應用 ............................ 26 4.3 動態規劃程式 ............................................................................................ 37 4.4 參數設定與模型測試 ................................................................................ 38 4.5 模型驗證與可改善方向 ............................................................................ 40 第五章 考量參考價格但參數未知的自適應訓練動態訂價模型 ........................ 47 5.1 問題架構與相關假設 ................................................................................ 47 5.2 動態規劃模型 ............................................................................................ 47 5.2.1 自適應訓練 .................................................................................... 47 5.3 動態規劃程式 ............................................................................................ 48 5.4 參數設定與模型測試 ................................................................................ 48 5.5 模型驗證 .................................................................................................... 52 第六章 模型驗證與數值分析 ................................................................................ 62 6.1 模型演算法流程 ........................................................................................ 62 6.2 模型驗證環境與方法說明 ........................................................................ 63 6.3 第一種模型之模擬結果與數值分析 ........................................................ 64 6.4 第二種模型之模擬結果與數值分析 ........................................................ 90 6.5 小結 .......................................................................................................... 117 第七章 結論與未來研究方向 .............................................................................. 119 7.1 結論 .......................................................................................................... 119 7.2 未來研究方向 .......................................................................................... 119 REFERENCE ............................................................................................................... 120 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 動態訂價 | zh_TW |
| dc.subject | 參考價格 | zh_TW |
| dc.subject | 多項羅吉特模型 | zh_TW |
| dc.subject | 需求參數估計 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 自適應訓練 | zh_TW |
| dc.subject | Multinomial logit model | en |
| dc.subject | Dynamic pricing | en |
| dc.subject | Reference price | en |
| dc.subject | Adaptive training | en |
| dc.subject | Deep learning | en |
| dc.subject | Demand parameters estimation | en |
| dc.title | 考量參考價格之需求參數估計與動態訂價 | zh_TW |
| dc.title | Demand Parameters Estimation and Dynamic Pricing Considering Reference Prices | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;周育樂 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Huang;Ywh-Leh Chou | en |
| dc.subject.keyword | 動態訂價,參考價格,多項羅吉特模型,需求參數估計,深度學習,自適應訓練, | zh_TW |
| dc.subject.keyword | Dynamic pricing,Reference price,Multinomial logit model,Demand parameters estimation,Deep learning,Adaptive training, | en |
| dc.relation.page | 123 | - |
| dc.identifier.doi | 10.6342/NTU202403513 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-08-08 | - |
| 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 | |
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