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題名 新產品的動態採購：隨機規劃與情境樹學習
Dynamic procurement of new products：Stochastic programming & scenario tree learning作者 張家瑜
Chang, Chia-Yu貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
張家瑜
Chang, Chia-Yu關鍵詞 新產品
動態採購策略隨機最佳化
情境樹
Residual tree
Neural gas
Covariate-free
New products
Stochastic programming of dynamic procurement
Scenario tree
Residual tree
Neural gas
Covariate-free日期 2021 上傳時間 4-八月-2021 14:47:53 (UTC+8) 摘要 本研究的貢獻在於提出一個更加貼合企業實際使用情況的新產品動態採購問題決策方法。現存許多關於需求預測的方法或許手段各異，卻往往因新產品缺乏所需歷史資料的關係而無法實際應用，為此一些研究提出利用產品間相似性預測新產品需求的方法，這些方法使用與新產品相似的過去產品資料解決了新產品缺乏歷史資料的問題，爾後由 Ban et al. (2019)提出的 Residual tree 則在此基礎上使用相似產品的 Covariates 並整合情境樹與最佳化，從單純的新產品需求預測進一步到了動態採購策略的規劃，而本研究延此思路提出了使用 Neural gas(Martinetz &Schulten, 1991)這一 Covariate-free 的演算法學習新產品需求情境樹以使用數學規劃求得採購策略的方法。一項跨足各行業領域的研究調查指出，新產品帶來的利潤大約占企業總體利潤的百分之二十五 (Cooper &Edgett, 2012)，足見新產品對於一間企業的重要性，雖該研究對象主要為美國企業，卻也不失為一項有參考性的指標，因而如何選用適當的方法對新產品進行更好地採購決策規劃對企業來說是相當重要的議題。本研究以成本表現與運算時間兩項指標作為主要探討方法可行性與實務價值的依據，經模擬實驗分析，我們所提出的方法於前述兩項指標上的結果確實不遜於作為比較對象的 Residual tree 演算法，對於新產品的動態採購規劃提供一個更具實務價值的選項。
The contribution of this study is to propose a method for making decisions on dynamic procurement problems for new products that is more suitable for the actual use of companies. Existing methods for demand forecasting may be based on different approaches, but they are often not practically applicable due to the lack of historicaldata required for new products. For this reason, some studies have proposed methods to forecast the demand for new products using the similarity between products, whichsolves the problem of the lack of historical data by using data of similar products sold in the past. Later, the residual tree algorithm proposed by Ban et al. (2019) usescovariates of similar products and integrates scenario tree with optimization to move from merely making demand forecasting to procurement decisions for new products.Our study extends this idea by proposing the use of the neural gas algorithm(Martinetz&Schulten, 1991), which is covariate-free, for learning new product demand trees tomake procurement decisions using mathematical programming. A cross-industry study has shown that new products account for approximately 25% of a company`s total profit(Cooper &Edgett, 2012), which shows the importance of new products to a company. Although the study is focused on U.S. companies, it still has some reference value. Therefore, it is essential for companies to choose an appropriate method to make better procurement decisionsfor new products. In this study, the feasibility and practicalvalue of the proposed method are based on two indicators, cost performance and computation time, and the results of the proposed method are comparable to those of the residual tree algorithm. Based on the simulations, the performance of our proposed method on the two indicators is indeed as good as the residual tree algorithm as acomparator, providing a more practical option for the dynamic procurement problem of new products.參考文獻 Baardman, L., Levin, I., Perakis, G., &Singhvi, D. (2018). Leveraging Comparables for New Product Sales Forecasting. Production and Operations Management, 27(12), 2340–2343.Ban, G. Y., Gallien, J., &Mersereau, A. J. (2019). Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing and Service Operations Management, 21(4), 798–815.Calfa, B. A., Agarwal, A., Grossmann, I. E., &Wassick, J. M. (2014). Data-driven multi-stage scenario tree generation via statistical property and distribution matching. Computers and Chemical Engineering, 68, 7–23.Cooper, R. G., &Edgett, S. J. (2012). Best Practices in the idea-to-launch process and its governance. Research Technology Management, 55(2), 43–54.Defourny, B., Ernst, D., &Wehenkel, L. (2011). Multistage stochastic programming: A scenario tree based approach to planning under uncertainty. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, 97–143.Fattahi, M., &Govindan, K. (2020). Data-Driven Rolling Horizon Approach for Dynamic Design of Supply Chain Distribution Networks under Disruption and Demand Uncertainty. Decision Sciences, 0(0), 1–31.Fei, X., Gülpınar, N., &Branke, J. (2019). Efficient solution selection for two-stage stochastic programs. European Journal of Operational Research, 277(3), 918–929.Høyland, K., &Wallace, S. W. (2001). Generating Scenario Trees for Multistage Decision Problems. Management Science, 47(2), 205–336.Hu, K., Acimovic, J., Erize, F., Thomas, D. J., &VanMieghem, J. A. (2019). Forecasting new product life cycle curves: Practical approach and empirical analysis. Manufacturing and Service Operations Management, 21(1), 66–85.Kouwenberg, R. (2004). Scenario generation and stochastic programming models for asset liability management. Polyhedron, 23(17 SPEC.ISS.), 2659–2664.Latorre, J. M., Cerisola, S., &Ramos, A. (2007). Clustering algorithms for scenario tree generation: Application to natural hydro inflows. European Journal of Operational Research, 181(3), 1339–1353.Martinetz, T. M., Berkovich, S. G., &Schulten, K. J. (1993). “Neural-Gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Transactions on Neural Networks, 4(4), 558–569.Martinetz, T. M., &Schulten, K. (1991). A “Neural-Gas” Network Learns Topologies. In Artificial Neural Networks (Vol. 1, pp. 397–402).Ponomareva, K., Roman, D., &Date, P. (2015). An algorithm for moment-matching scenario generation with application to financial portfolio optimisation. European Journal of Operational Research, 240(3), 678–687.Turner, S., &Galelli, S. (2016). Building a reduced scenario tree for multi-stage stochastic programming. https://cran.r-project.org/web/packages/scenario/vignettes/buildtree.htmlXu, B., Zhong, P. A., Zambon, R. C., Zhao, Y., &Yeh, W. W. G. (2015). Scenario tree reduction in stochastic programming with recourse for hydropower operations. Water Resources Research, 51(8), 6359–6380. 描述 碩士
國立政治大學
資訊管理學系
108356015資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356015 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-Chun en_US dc.contributor.author (作者) 張家瑜 zh_TW dc.contributor.author (作者) Chang, Chia-Yu en_US dc.creator (作者) 張家瑜 zh_TW dc.creator (作者) Chang, Chia-Yu en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-八月-2021 14:47:53 (UTC+8) - dc.date.available 4-八月-2021 14:47:53 (UTC+8) - dc.date.issued (上傳時間) 4-八月-2021 14:47:53 (UTC+8) - dc.identifier (其他 識別碼) G0108356015 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136345 - 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) 108356015 zh_TW dc.description.abstract (摘要) 本研究的貢獻在於提出一個更加貼合企業實際使用情況的新產品動態採購問題決策方法。現存許多關於需求預測的方法或許手段各異，卻往往因新產品缺乏所需歷史資料的關係而無法實際應用，為此一些研究提出利用產品間相似性預測新產品需求的方法，這些方法使用與新產品相似的過去產品資料解決了新產品缺乏歷史資料的問題，爾後由 Ban et al. (2019)提出的 Residual tree 則在此基礎上使用相似產品的 Covariates 並整合情境樹與最佳化，從單純的新產品需求預測進一步到了動態採購策略的規劃，而本研究延此思路提出了使用 Neural gas(Martinetz &Schulten, 1991)這一 Covariate-free 的演算法學習新產品需求情境樹以使用數學規劃求得採購策略的方法。一項跨足各行業領域的研究調查指出，新產品帶來的利潤大約占企業總體利潤的百分之二十五 (Cooper &Edgett, 2012)，足見新產品對於一間企業的重要性，雖該研究對象主要為美國企業，卻也不失為一項有參考性的指標，因而如何選用適當的方法對新產品進行更好地採購決策規劃對企業來說是相當重要的議題。本研究以成本表現與運算時間兩項指標作為主要探討方法可行性與實務價值的依據，經模擬實驗分析，我們所提出的方法於前述兩項指標上的結果確實不遜於作為比較對象的 Residual tree 演算法，對於新產品的動態採購規劃提供一個更具實務價值的選項。 zh_TW dc.description.abstract (摘要) The contribution of this study is to propose a method for making decisions on dynamic procurement problems for new products that is more suitable for the actual use of companies. Existing methods for demand forecasting may be based on different approaches, but they are often not practically applicable due to the lack of historicaldata required for new products. For this reason, some studies have proposed methods to forecast the demand for new products using the similarity between products, whichsolves the problem of the lack of historical data by using data of similar products sold in the past. Later, the residual tree algorithm proposed by Ban et al. (2019) usescovariates of similar products and integrates scenario tree with optimization to move from merely making demand forecasting to procurement decisions for new products.Our study extends this idea by proposing the use of the neural gas algorithm(Martinetz&Schulten, 1991), which is covariate-free, for learning new product demand trees tomake procurement decisions using mathematical programming. A cross-industry study has shown that new products account for approximately 25% of a company`s total profit(Cooper &Edgett, 2012), which shows the importance of new products to a company. Although the study is focused on U.S. companies, it still has some reference value. Therefore, it is essential for companies to choose an appropriate method to make better procurement decisionsfor new products. In this study, the feasibility and practicalvalue of the proposed method are based on two indicators, cost performance and computation time, and the results of the proposed method are comparable to those of the residual tree algorithm. Based on the simulations, the performance of our proposed method on the two indicators is indeed as good as the residual tree algorithm as acomparator, providing a more practical option for the dynamic procurement problem of new products. en_US dc.description.tableofcontents 第一章 緒論 1第二章 動態採購與隨機規劃 3第一節 新產品採購最佳化問題 3第二節 情境樹 4第三節 Residual tree 演算法 7第三章 情境樹學習 12第一節 最佳化建構方法 12第二節 Neural gas method 14第四章 模擬分析 21第一節 實驗設計 21第二節 分析結果 23第五章 結論 31第六章 參考文獻 33 zh_TW dc.format.extent 2310656 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356015 en_US dc.subject (關鍵詞) 新產品 zh_TW dc.subject (關鍵詞) 動態採購策略隨機最佳化 zh_TW dc.subject (關鍵詞) 情境樹 zh_TW dc.subject (關鍵詞) Residual tree zh_TW dc.subject (關鍵詞) Neural gas zh_TW dc.subject (關鍵詞) Covariate-free zh_TW dc.subject (關鍵詞) New products en_US dc.subject (關鍵詞) Stochastic programming of dynamic procurement en_US dc.subject (關鍵詞) Scenario tree en_US dc.subject (關鍵詞) Residual tree en_US dc.subject (關鍵詞) Neural gas en_US dc.subject (關鍵詞) Covariate-free en_US dc.title (題名) 新產品的動態採購：隨機規劃與情境樹學習 zh_TW dc.title (題名) Dynamic procurement of new products：Stochastic programming & scenario tree learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Baardman, L., Levin, I., Perakis, G., &Singhvi, D. (2018). Leveraging Comparables for New Product Sales Forecasting. Production and Operations Management, 27(12), 2340–2343.Ban, G. Y., Gallien, J., &Mersereau, A. J. (2019). Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing and Service Operations Management, 21(4), 798–815.Calfa, B. A., Agarwal, A., Grossmann, I. E., &Wassick, J. M. (2014). Data-driven multi-stage scenario tree generation via statistical property and distribution matching. Computers and Chemical Engineering, 68, 7–23.Cooper, R. G., &Edgett, S. J. (2012). Best Practices in the idea-to-launch process and its governance. Research Technology Management, 55(2), 43–54.Defourny, B., Ernst, D., &Wehenkel, L. (2011). Multistage stochastic programming: A scenario tree based approach to planning under uncertainty. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, 97–143.Fattahi, M., &Govindan, K. (2020). Data-Driven Rolling Horizon Approach for Dynamic Design of Supply Chain Distribution Networks under Disruption and Demand Uncertainty. Decision Sciences, 0(0), 1–31.Fei, X., Gülpınar, N., &Branke, J. (2019). Efficient solution selection for two-stage stochastic programs. European Journal of Operational Research, 277(3), 918–929.Høyland, K., &Wallace, S. W. (2001). Generating Scenario Trees for Multistage Decision Problems. Management Science, 47(2), 205–336.Hu, K., Acimovic, J., Erize, F., Thomas, D. J., &VanMieghem, J. A. (2019). Forecasting new product life cycle curves: Practical approach and empirical analysis. Manufacturing and Service Operations Management, 21(1), 66–85.Kouwenberg, R. (2004). Scenario generation and stochastic programming models for asset liability management. Polyhedron, 23(17 SPEC.ISS.), 2659–2664.Latorre, J. M., Cerisola, S., &Ramos, A. (2007). Clustering algorithms for scenario tree generation: Application to natural hydro inflows. European Journal of Operational Research, 181(3), 1339–1353.Martinetz, T. M., Berkovich, S. G., &Schulten, K. J. (1993). “Neural-Gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Transactions on Neural Networks, 4(4), 558–569.Martinetz, T. M., &Schulten, K. (1991). A “Neural-Gas” Network Learns Topologies. In Artificial Neural Networks (Vol. 1, pp. 397–402).Ponomareva, K., Roman, D., &Date, P. (2015). An algorithm for moment-matching scenario generation with application to financial portfolio optimisation. European Journal of Operational Research, 240(3), 678–687.Turner, S., &Galelli, S. (2016). Building a reduced scenario tree for multi-stage stochastic programming. https://cran.r-project.org/web/packages/scenario/vignettes/buildtree.htmlXu, B., Zhong, P. A., Zambon, R. C., Zhao, Y., &Yeh, W. W. G. (2015). Scenario tree reduction in stochastic programming with recourse for hydropower operations. Water Resources Research, 51(8), 6359–6380. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101038 en_US
