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題名 條件變分自編碼器於分佈學習和報童模型
Conditional Variational Autoencoder for Distribution Learning and Newsvendor Model
作者 蔡孟臻
Tsai, Meng-Zhen
貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
蔡孟臻
Tsai, Meng-Zhen
關鍵詞 條件變分自編碼器
報童問題
需求生成
兩階段補貨
分佈學習
Conditional Variational Autoencoder
Newsvendor Problem
Demand Generation
Two-Stage Replenishment
Distribution Learning
日期 2025
上傳時間 4-Aug-2025 14:25:37 (UTC+8)
摘要 本研究聚焦於營運決策中的兩階段報童問題，提出一套統一式條件生成模型，以建構精確的未來需求分布並支援補貨決策。為因應不同補貨時點所面臨的變長需求序列問題，本文設計結合條件變分自編碼器（Conditional Variational Autoencoder, CVAE）與長短期記憶網路（Long Short-Term Memory, LSTM）之深度生成架構，能根據任意觀測歷史資訊生成滿足條件分布的需求樣本。研究首先於單階段訂價報童問題中驗證 CVAE 於需求重建上的可行性，作為模型基礎能力之驗證，進一步延伸至二階段補貨情境中。實驗採用模擬資料，並與隨機森林模型比較，透過 Wasserstein 距離與 Kolmogorov– Smirnov 統計量評估分布準確性，並分析補貨決策的平均利潤表現。結果顯示所提方法於樣本準確性與決策效益皆具顯著優勢，展現其於需求不確定性建模與營運分析整合應用之潛力。
This study addresses the two-stage newsvendor problem in operations decision-making by proposing a unified conditional generative model to accurately capture the conditional distribution of future demand and support replenishment decisions. To handle the challenge of variable-length demand sequences observed at different replenishment points, we design a deep generative framework that integrates a Conditional Variational Autoencoder (CVAE) with a Long Short-Term Memory (LSTM) network. This model generates conditional demand samples based on any observed historical information. We first validate the feasibility of using CVAE to reconstruct price-driven demand distributions in a single-stage setting, serving as a foundation for model capability, before extending the method to two-stage replenishment scenarios. Simulation experiments compare the proposed model against Random Forest benchmarks. Evaluation metrics include the Wasserstein distance and Kolmogorov– Smirnov (KS) statistic for distribution accuracy, as well as average profit performance for decision quality. Results demonstrate that the proposed method significantly outperforms traditional models in both generative accuracy and downstream decision effectiveness, highlighting its potential for integrating uncertainty modeling with operational analytics.
參考文獻 Aldaihani, M. M. and Darwish, M. A. (2016). Service level enhancement in newsvendor model. Computers & Industrial Engineering, 98:164–170. Bishop, C. M. and Nasrabadi, N. M. (2006). Pattern recognition and machine learning, volume 4. Springer. Brégère, M. and Bessa, R. J. (2020). Simulating tariff impact in electrical energy consumption profiles with conditional variational autoencoders. IEEE Access, 8:131949–131966. Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A. (2018). Understanding disentangling in β-vae. arXiv preprint arXiv:1804.03599. Contreras, J., Espinola, R., Nogales, F., and Conejo, A. (2003). Arima models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3):1014–1020. DeYong, G. D. (2020). The price-setting newsvendor: review and extensions. International Journal of Production Research, 58(6):1776–1804. Fang, L., Zeng, T., Liu, C., Bo, L., Dong, W., and Chen, C. (2021). Transformer-based conditional variational autoencoder for controllable story generation. arXiv preprint arXiv:2101.00828. Gammelli, D., Wang, Y., Prak, D., Rodrigues, F., Minner, S., and Pereira, F. C. (2022). Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management. Transportation Research Part C: Emerging Technologies, 138:103571. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11):139–144. Harsha, P., Natarajan, R., and Subramanian, D. (2021). A prescriptive machine-learning framework to the price-setting newsvendor problem. Informs Journal on Optimization, 3(3):227–253. Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786):504–507. Hiransha, M., Gopalakrishnan, E., Menon, V. K., and Soman, K. (2018). Nse stock market prediction using deep-learning models. Procedia Computer Science, 132:1351–1362. International Conference on Computational Intelligence and Data Science. Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L., and Weinberger, K., editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc. Massey, F. J. (1951). The kolmogorov-smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253):68–78. Milner, J. M. and Kouvelis, P. (2005). Order quantity and timing flexibility in supply chains: The role of demand characteristics. Management Science, 51(6):970–985. Papalexopoulos, A. and Hesterberg, T. (1990). A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems, 5(4):1535–1547. Petruzzi, N. C. and Dada, M. (1999). Pricing and the newsvendor problem: A review with extensions. Operations research, 47(2):183–194. Smirnov, D., Herer, Y. T., and Avrahami, A. (2021). Two-phase newsvendor with optimally timed additional replenishment: Model, algorithm, case study. Production and Operations Management, 30(9):2871–2889. Sohn, K., Lee, H., and Yan, X. (2015). Learning structured output representation using deep conditional generative models. Advances in neural information processing systems, 28. Villani, C. (2009). Optimal transport: old and new, volume 338. Springer. Wager, S., Hastie, T., and Efron, B. (2014). Confidence intervals for random forests: The jackknife and the infinitesimal jackknife. The journal of machine learning research, 15(1):1625–1651. Wang, C., Sharifnia, E., Gao, Z., Tindemans, S. H., and Palensky, P. (2022). Generating multivariate load states using a conditional variational autoencoder. Electric Power Systems Research, 213:108603. Yan, X., Yang, J., Sohn, K., and Lee, H. (2016). Attribute2image: Conditional image generation from visual attributes. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 776–791. Springer.
描述 碩士
國立政治大學
資訊管理學系
112356008
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112356008
資料類型 thesis
dc.contributor.advisor 莊皓鈞<br>周彥君zh_TW
dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chunen_US
dc.contributor.author (Authors) 蔡孟臻zh_TW
dc.contributor.author (Authors) Tsai, Meng-Zhenen_US
dc.creator (作者) 蔡孟臻zh_TW
dc.creator (作者) Tsai, Meng-Zhenen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:25:37 (UTC+8)-
dc.date.available 4-Aug-2025 14:25:37 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:25:37 (UTC+8)-
dc.identifier (Other Identifiers) G0112356008en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158568-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 112356008zh_TW
dc.description.abstract (摘要) 本研究聚焦於營運決策中的兩階段報童問題，提出一套統一式條件生成模型，以建構精確的未來需求分布並支援補貨決策。為因應不同補貨時點所面臨的變長需求序列問題，本文設計結合條件變分自編碼器（Conditional Variational Autoencoder, CVAE）與長短期記憶網路（Long Short-Term Memory, LSTM）之深度生成架構，能根據任意觀測歷史資訊生成滿足條件分布的需求樣本。研究首先於單階段訂價報童問題中驗證 CVAE 於需求重建上的可行性，作為模型基礎能力之驗證，進一步延伸至二階段補貨情境中。實驗採用模擬資料，並與隨機森林模型比較，透過 Wasserstein 距離與 Kolmogorov– Smirnov 統計量評估分布準確性，並分析補貨決策的平均利潤表現。結果顯示所提方法於樣本準確性與決策效益皆具顯著優勢，展現其於需求不確定性建模與營運分析整合應用之潛力。zh_TW
dc.description.abstract (摘要) This study addresses the two-stage newsvendor problem in operations decision-making by proposing a unified conditional generative model to accurately capture the conditional distribution of future demand and support replenishment decisions. To handle the challenge of variable-length demand sequences observed at different replenishment points, we design a deep generative framework that integrates a Conditional Variational Autoencoder (CVAE) with a Long Short-Term Memory (LSTM) network. This model generates conditional demand samples based on any observed historical information. We first validate the feasibility of using CVAE to reconstruct price-driven demand distributions in a single-stage setting, serving as a foundation for model capability, before extending the method to two-stage replenishment scenarios. Simulation experiments compare the proposed model against Random Forest benchmarks. Evaluation metrics include the Wasserstein distance and Kolmogorov– Smirnov (KS) statistic for distribution accuracy, as well as average profit performance for decision quality. Results demonstrate that the proposed method significantly outperforms traditional models in both generative accuracy and downstream decision effectiveness, highlighting its potential for integrating uncertainty modeling with operational analytics.en_US
dc.description.tableofcontents 摘要 i Abstract ii 目次 iii 圖次 v 表次 vii 第一章 緒論 1 第二章 文獻探討 3 第一節 自動編碼器 (Auto Encoder, AE) 3 第二節 條件變分自動編碼器 (Conditional Variational Auto Encoder, CVAE) 5 第三章 訂價報童問題的最佳化 8 第一節 問題定義 8 第二節 實驗設計與模型架構 10 第三節 實驗結果 12 一、 模型效能比較: 基本 CVAE 與改進 CVAE 12 二、 泛化能力評估：內插與外推測試 14 第四章 兩階段報童問題最佳化 19 第一節 問題定義 19 第二節 實驗設計 20 一、 模型架構設計 20 二、 模擬資料產生與設計 23 第三節 實驗結果 26 第四節 利潤比較分析 29 第五章 結論與建議 32 第一節 研究結論 32 第二節 學術貢獻 33 第三節 研究限制與未來方向 34 參考文獻 35zh_TW
dc.format.extent 4830342 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112356008en_US
dc.subject (關鍵詞) 條件變分自編碼器zh_TW
dc.subject (關鍵詞) 報童問題zh_TW
dc.subject (關鍵詞) 需求生成zh_TW
dc.subject (關鍵詞) 兩階段補貨zh_TW
dc.subject (關鍵詞) 分佈學習zh_TW
dc.subject (關鍵詞) Conditional Variational Autoencoderen_US
dc.subject (關鍵詞) Newsvendor Problemen_US
dc.subject (關鍵詞) Demand Generationen_US
dc.subject (關鍵詞) Two-Stage Replenishmenten_US
dc.subject (關鍵詞) Distribution Learningen_US
dc.title (題名) 條件變分自編碼器於分佈學習和報童模型zh_TW
dc.title (題名) Conditional Variational Autoencoder for Distribution Learning and Newsvendor Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Aldaihani, M. M. and Darwish, M. A. (2016). Service level enhancement in newsvendor model. Computers & Industrial Engineering, 98:164–170. Bishop, C. M. and Nasrabadi, N. M. (2006). Pattern recognition and machine learning, volume 4. Springer. Brégère, M. and Bessa, R. J. (2020). Simulating tariff impact in electrical energy consumption profiles with conditional variational autoencoders. IEEE Access, 8:131949–131966. Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A. (2018). Understanding disentangling in β-vae. arXiv preprint arXiv:1804.03599. Contreras, J., Espinola, R., Nogales, F., and Conejo, A. (2003). Arima models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3):1014–1020. DeYong, G. D. (2020). The price-setting newsvendor: review and extensions. International Journal of Production Research, 58(6):1776–1804. Fang, L., Zeng, T., Liu, C., Bo, L., Dong, W., and Chen, C. (2021). Transformer-based conditional variational autoencoder for controllable story generation. arXiv preprint arXiv:2101.00828. Gammelli, D., Wang, Y., Prak, D., Rodrigues, F., Minner, S., and Pereira, F. C. (2022). Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management. Transportation Research Part C: Emerging Technologies, 138:103571. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11):139–144. Harsha, P., Natarajan, R., and Subramanian, D. (2021). A prescriptive machine-learning framework to the price-setting newsvendor problem. Informs Journal on Optimization, 3(3):227–253. Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786):504–507. Hiransha, M., Gopalakrishnan, E., Menon, V. K., and Soman, K. (2018). Nse stock market prediction using deep-learning models. Procedia Computer Science, 132:1351–1362. International Conference on Computational Intelligence and Data Science. Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L., and Weinberger, K., editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc. Massey, F. J. (1951). The kolmogorov-smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253):68–78. Milner, J. M. and Kouvelis, P. (2005). Order quantity and timing flexibility in supply chains: The role of demand characteristics. Management Science, 51(6):970–985. Papalexopoulos, A. and Hesterberg, T. (1990). A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems, 5(4):1535–1547. Petruzzi, N. C. and Dada, M. (1999). Pricing and the newsvendor problem: A review with extensions. Operations research, 47(2):183–194. Smirnov, D., Herer, Y. T., and Avrahami, A. (2021). Two-phase newsvendor with optimally timed additional replenishment: Model, algorithm, case study. Production and Operations Management, 30(9):2871–2889. Sohn, K., Lee, H., and Yan, X. (2015). Learning structured output representation using deep conditional generative models. Advances in neural information processing systems, 28. Villani, C. (2009). Optimal transport: old and new, volume 338. Springer. Wager, S., Hastie, T., and Efron, B. (2014). Confidence intervals for random forests: The jackknife and the infinitesimal jackknife. The journal of machine learning research, 15(1):1625–1651. Wang, C., Sharifnia, E., Gao, Z., Tindemans, S. H., and Palensky, P. (2022). Generating multivariate load states using a conditional variational autoencoder. Electric Power Systems Research, 213:108603. Yan, X., Yang, J., Sohn, K., and Lee, H. (2016). Attribute2image: Conditional image generation from visual attributes. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 776–791. Springer.zh_TW