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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 周雍強(Yon-Chun Chou) | |
dc.contributor.author | Yu-Chun Pan | en |
dc.contributor.author | 潘昱均 | zh_TW |
dc.date.accessioned | 2021-06-08T04:02:07Z | - |
dc.date.copyright | 2018-08-09 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-06 | |
dc.identifier.citation | [1] 于妙善. (2017). 在需求與供應不確定下自行車共享系統多站點間之供需媒合. 臺灣大學工業工程學研究所學位論文, 1-85.
[2] 王俊偉. '以系統模擬探討公共自行車租借系統之建置及營運策略.' 成功大學資訊管理研究所學位論文 (2011): 1-89. [3] 李宏毅. (2016). 專題-人工智慧與 AlphaGo 什麼是深度學習. 數理人文, (10). [4] 賴勁丞. '基於站點相依性之公共自行車調度策略研究.' 臺灣大學土木工程學研究所學位論文 (2016): 1-95. [5] Angkiriwang, R., Pujawan, I. N., & Santosa, B. (2014). Managing uncertainty through supply chain flexibility: reactive vs. proactive approaches. Production & Manufacturing Research, 2(1), 50-70. [6] Ali, S. M., & Nakade, K. (2015). A mathematical optimization approach to supply chain disruptions management considering disruptions to suppliers and distribution centers. operations and supply chain management, 8(2), 57-66. [7] Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2015, February). The loss surfaces of multilayer networks. In Artificial Intelligence and Statistics (pp. 192-204). [8] Erdem, A. S., & Özekici, S. (2002). Inventory models with random yield in a random environment. International Journal of Production Economics, 78(3), 239-253. [9] Giri, B. C., Bardhan, S., & Maiti, T. (2016). Coordinating a three-layer supply chain with uncertain demand and random yield. International Journal of Production Research, 54(8), 2499-2518. [10] Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. [11] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [12] Lin, C. C., & Wang, T. H. (2011). Build-to-order supply chain network design under supply and demand uncertainties. Transportation Research Part B: Methodological, 45(8), 1162-1176. [13] Lin, J. R., & Yang, T. H. (2011). Strategic design of public bicycle sharing systems with service level constraints. Transportation research part E: logistics and transportation review, 47(2), 284-294. [14] Miranda, P. A., & Garrido, R. A. (2009). Inventory service-level optimization within distribution network design problem. International Journal of production economics, 122(1), 276-285. [15] Mont, O. K. (2002). Clarifying the concept of product–service system. Journal of cleaner production, 10(3), 237-245. [16] Nasr, W. W., Salameh, M. K., & Moussawi-Haidar, L. (2012). Transshipment and safety stock under stochastic supply interruption in a production system. Computers & Industrial Engineering, 63(1), 274-284. [17] Schmitt, A. J., Snyder, L. V., & Shen, Z. J. M. (2010). Inventory systems with stochastic demand and supply: Properties and approximations. European Journal of Operational Research, 206(2), 313-328. [18] Stathakis, D. (2009). How many hidden layers and nodes?. International Journal of Remote Sensing, 30(8), 2133-2147. [19] Tukker, A. (2004). Eight types of product–service system: eight ways to sustainability? Experiences from SusProNet. Business strategy and the environment, 13(4), 246-260. [20] Wang, C. X. (2009). Random yield and uncertain demand in decentralised supply chains under the traditional and VMI arrangements. International Journal of Production Research, 47(7), 1955-1968. [21] Weiss, N. A. (2001). Introductory statistics. Boston: Addison-Wesley(6th ed),584 [22] Wong, M. T. N. (2004). Implementation of innovative product service systems in the consumer goods industry (Doctoral dissertation, University of Cambridge). [23] Yang, T. H., Lin, J. R. and Chang, Y. C. Strategic design of public bicycle sharingsystems incorporating with bicycle stocks considerations. Proceeding of The 40th International Conference on Computers and Industrial Engineering, 25-28, 2010 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22093 | - |
dc.description.abstract | 供應鏈與物流當中存在許多供應不穩或需求擾動的不確定因素。而供應未能配合需求的情況下就會產生利用率以及經濟的損失,像是原料短缺或是生產不穩造成無法完致使發生違約賠償,甚至商譽及市占率的損失,另外需求的擾動也會造成存貨堆積產生跌價損失。
本文將對於供需兩端同時進行建模處理,供需模型的概念是來自於共享單車的存貨調度。因為人潮的需求隨機性會使得存貨產生分布不均勻的現象,藉由站點的存量水準決定出供應需求兩種不同性質的類別,再將存量較多的供應性質站點調度一些存貨到存貨量較少的需求站點。 且利用深度學習來代替原本一連串的處理與計算過程,簡化中間的流程與增加模型的彈性,在面對不同的情境不同的調度情況出現時不必再經由一連串的步驟來得到最佳解。只需透過建立好的深度學習模型就可以得到預測的解,並對深度學習在此問題時的建模提出建模流程以及對不同資料格式與變數檢定得出一個完整的深度學習模型。最後提出一個衡量模型的指標讓使用者可以決定是否採用該模型,再藉由此指標對前面建立的深度學習模型的進行檢測表現,驗證該模型是可以接受來做為替代資料前處理與數學規劃求解的方法。 | zh_TW |
dc.description.abstract | There are many uncertain supply and uncertain demand supply issues in supply chain management and logistics process, such as supply disturbance and demand instability; manufacturing process, material delivery, production quality and market order stochastic. Abundant of previous papers discuss about one side uncertainty, and the research work on both side uncertainties is not a lot. A bike-sharing concept of supply and demand, which is determine surplus side or deficit side by the stock level of the site. Surplus sites with more stocks supply some stocks to deficit station. The basic data means the setting of surplus deficit site using to generate sample probability, and then the supply and demand functions required by matching model are obtained through conversion. This matching uncertainty supply demand model can be separate into two stage model: discontinuous dispatching model and continuous dispatching model. Simulation method can generate many cases, and these cases can be used to explore the feature of random supply and demand cases. Parameter settings and model output discussion would give out reason to choose variable to use in machine learning model. Machine learning model can fit the function of the data pre-processing and scheduling logic behind the data. There are three issues about data format, variable in training data and data volume are researched. After the best model under this research been found, this model could be applied to obtain relocation information without data pre-processing and optimization calculating work. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T04:02:07Z (GMT). No. of bitstreams: 1 ntu-107-R05546028-1.pdf: 3096536 bytes, checksum: 57753280d0f42bf998401b3ef4eaa66a (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 摘要 i
Abstract ii 目錄 iii 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的與方法 1 1.3 本文架構 2 第二章 文獻回顧 3 2.1 供需不確定的媒合文獻 3 2.2 供需媒合模型回顧 14 2.2.1 資料前處理 15 2.2.2 數學規劃模型 18 2.3 數值模擬 20 2.4 深度學習 21 第三章 供需調度模型分析 25 3.1 資料產生 25 3.1.1 供需數值模擬基本設定 26 3.1.2 隨機模擬各站點的供需數量 28 3.2 資料前處理 29 3.2.1 累積供應量機率 29 3.2.2 效用值與效用增幅 30 3.3 需調度模型 31 3.4 下限值調整分析 33 3.5 需求函數型態與統計分析 41 3.5.1 個別需求函數性質統計 45 3.5.2 組合關係下的需求加總 46 3.6 章節小結 46 第四章 深度學習模型 48 4.1 建立深度學習模型的設定 49 4.2 資料格式之影響 55 4.2.1 個別站點的格式 57 4.2.2 串接資料格式 63 4.2.3 兩資料格式透過多個例子比較 65 4.3 資料變數 72 4.4 訓練資料量 75 4.5 多種機器學習比較 79 第五章 結論與建議 83 附錄 1: LINGO程式碼 84 附錄 2: LINGO求解結果 86 附錄 3: 資料前處理 88 附錄 4: Gurobi求解 112 附錄 5: Keras深度學習 117 參考文獻 125 | |
dc.language.iso | zh-TW | |
dc.title | 應用深度學習於需求與供應不確定之調度問題 | zh_TW |
dc.title | Application of deep neural network to the supply and demand matching problem | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 洪一薰(I-Hsuan Hong),陳曜鴻(Yao-Hung Chen) | |
dc.subject.keyword | 不確定供應與需求,供需調度,機器學習,深度神經網路, | zh_TW |
dc.subject.keyword | uncertain demand and uncertain supply,supply and demand matching,machine learning,deep neural network, | en |
dc.relation.page | 127 | |
dc.identifier.doi | 10.6342/NTU201802535 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2018-08-07 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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