企業為了應對市場上快速的步調，為了有效率地推動企業內的業務營運，會採取專案型式的編制，來應對具有特殊性與目標導向的任務。由於專案的規模與預算龐大，專案業務又多會涉及到跨部門，工作業務複雜度高，讓追蹤管理專案具有一定的難度。專案經理無法僅透過人為經驗判斷，分析專案未來的走向。實獲值管理廣泛被企業採用作為追蹤專案進度的工具，使用實獲值管理得以在有限的資訊下，在不同的專案執行階段中，以當前資訊預測專案完工成本與專案完工時間。專案經理在執行專案階段可透過準確預測的專案完工成本與專案完工時間，輔助專案經理控制專案進度、成本與資源。
　　為了建構準確度高的專案實獲值預測系統，本研究以過去實獲值管理研究作為基礎，使用長短期記憶網路與貝氏最佳化方法建立研究模型EACLSTM。首先，將專案資料轉換成類神經網路所需的型態，將專案資料分為三大輸入層輸入類神經網路模型，模型輸出可得到預測的專案最終完工成本與專案最終完工時間。同時，本研究也將模型架構之超參數進行貝氏最佳化，獲得模型最佳超參數組合，以建構最佳的類神經網路模型。
　　最後以專案資料測試集驗證本模型的績效表現，並將研究模型EACLSTM結果、其他類神經網路模型以及傳統的實獲值管理預測方法三者做比較。研究模型EACLSTM之預測結果優於其他類神經網路模型的預測能力，與傳統實獲值的預測方法比較，發現傳統實獲值預測雖然在完工成本預測方面有良好的預測結果，但在完工時間的預測上為本研究所提出的預測方法較佳。本研究模型同時提供專案完工時間與完工成本的預測，並同時兼顧兩項預測指標的品質，更可以避免傳統實獲值方法過度依賴單一績效指標而產生的預測誤差，模型輸出將提供給專案經理能以本研究模型EACLSTM結果輔助決策，提供有品質的預測結果供企業參考。

To drive business operations and adapt to market dynamics, organizations adopt project-based approaches to address tasks with specific goals and distinct characteristics. Earned Value Management (EVM) is widely adopted by enterprises as a tool for monitoring project progress. By leveraging EVM, organizations can make forecast regarding project completion costs and time during different stages of project execution. To implement a project earned value forecasting system, this study utilizes the Long Short-Term Memory network and Bayesian optimization method to develop the proposed EACLSTM model. The project data is transformed and input into the neural network model, which is structured with three distinct subnetworks within the neural network model. The model outputs forecasts for project completion cost and completion time. Furthermore, Bayesian optimization is utilized to optimize the hyperparameters of the model, resulting in the construction of an optimal model architecture. The results demonstrate that the EACLSTM model outperforms other neural network models. Furthermore, the EACLSTM model excels in forecasting completion time compared to the methods mentioned in literature, while maintaining favorable cost forecasting outcomes. This EACLSTM model provides simultaneous forecasting for project completion time and completion cost, while also considering the quality of both forecast indicators. It helps to avoid forecast errors resulting from the traditional earned value method's over-reliance on a single performance indicator. The model’s output can assist the project managers to make decision on control stage of project management cycle.

Acebes, F., Pajares, J., Galan, J. M., & Lopez Paredes, A. (2014). A new approach for project control under uncertainty. Going back to the basics. International Journal of Project Management, 32(3), 423-434. https://doi.org/10.1016/j.ijproman.2013.08.003
Acebes, F., Poza, D., González Varona, J. M., & López Paredes, A. (2021). Stochastic earned duration analysis for project schedule management. Engineering. https://doi.org/10.1016/j.eng.2021.07.019
Akhbari, M. (2018). Project time and cost forecasting using monte carlo simulation and artificial neural networks. International Journal of Industrial Engineering & Production Research, 29(2), 231-239. http://ijiepr.iust.ac.ir/article-1-793-en.pdf
Anbari, F. T. (2003). Earned value project management method and extensions. Project Management Journal, 34(4), 12-23. https://doi.org/10.1177/875697280303400403
Aramali, V., Sanboskani, H., Gibson, G. E., El Asmar, M., & Cho, N. A. (2022). Forward-looking state-of-the-art review on earned value management systems: The disconnect between academia and industry. Journal of Management in Engineering, 38(3), 21. https://doi.org/10.1061/(asce)me.1943-5479.0001019
Atkinson, R. (1999). Project management: Cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project Management, 17(6), 337-342. https://doi.org/10.1016/S0263-7863(98)00069-6
Attalla, M., & Hegazy, T. (2003). Predicting cost deviation in reconstruction projects: Artificial neural network versus regression. Journal of Construction Engineering and Management-Asce, 129(4), 405-411. https://doi.org/10.1061/(asce)0733-9364(2003)129:4(405)
Batselier, J., & Vanhoucke, M. (2015). Construction and evaluation framework for a real-life project database. International Journal of Project Management, 33(3), 697-710. https://doi.org/10.1016/j.ijproman.2014.09.004
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2), 281–305. https://doi/10.5555/2188385.2188395
Chen, H. L., Chen, W. T., & Lin, Y. L. (2016). Earned value project management: Improving the predictive power of planned value. International Journal of Project Management, 34(1), 22-29. https://doi.org/10.1016/j.ijproman.2015.09.008
Chen, Y., Cheng, Q., Cheng, Y., Yang, H., & Yu, H. (2018). Applications of recurrent neural networks in environmental factor forecasting: A review. Neural Computation, 30(11), 2855-2881. https://doi.org/10.1162/neco_a_01134
Cheng, M. Y., Chang, Y. H., & Korir, D. (2019). Novel approach to estimating schedule to completion in construction projects using sequence and nonsequence learning. Journal of Construction Engineering and Management, 145(11), 17. https://doi.org/10.1061/(asce)co.1943-7862.0001697
Cheng, M. Y., Peng, H. S., Wu, Y. W., & Chen, T. L. (2010). Estimate at completion for construction projects using evolutionary support vector machine inference model. Automation in Construction, 19(5), 619-629. https://doi.org/10.1016/j.autcon.2010.02.008
Cioffi, D. F. (2006). Designing project management: A scientific notation and an improved formalism for earned value calculations. International Journal of Project Management, 24(2), 136-144. https://doi.org/10.1016/j.ijproman.2005.07.003
Fleming, Q. W., & Koppelman, J. M. (1994). The earned value concept: Back to the basics. PM Network, 8(11), 27-29. https://www.pmi.org/learning/library/earned-value-control-systems-criteria-3406
Hüsken, M., & Stagge, P. (2003). Recurrent neural networks for time series classification. Neurocomputing, 50, 223-235. https://doi.org/10.1016/S0925-2312(01)00706-8
Hall, N. G. (2012). Project management: Recent developments and research opportunities. Journal of Systems Science and Systems Engineering, 21(2), 129-143. https://doi.org/10.1007/s11518-012-5190-5
Hazir, O. (2015). A review of analytical models, approaches and decision support tools in project monitoring and control. International Journal of Project Management, 33(4), 808-815. https://doi.org/10.1016/j.ijproman.2014.09.005
He, F. F., Zhou, J. Z., Feng, Z. K., Liu, G. B., & Yang, Y. Q. (2019). A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with bayesian optimization algorithm. Applied Energy, 237, 103-116. https://doi.org/10.1016/j.apenergy.2019.01.055
Hegazy, T., & Ayed, A. (1998). Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124(3), 210-218. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(210)
Henderson, K. (2003). Earned schedule: A breakthrough extension to earned value theory? A retrospective analysis of real project data. The Measurable News, 1(2), 13-23. https://www.semanticscholar.org/paper/Earned-Schedule%3A-A-Breakthrough-Extension-to-Earned-Henderson/df37fbf95e9638c1a6c13ef5ef123897315413e7
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hutter, F., Hoos, H. H., & Leyton Brown, K. (2011). Sequential model-based optimization for general algorithm configuration. Paper presented at the International conference on learning and intelligent optimization.
Jin, X. B., Zheng, W. Z., Kong, J. L., Wang, X. Y., Bai, Y. T., Su, T. L., & Lin, S. (2021). Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization. Energies, 14(6), 18. https://doi.org/10.3390/en14061596
Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling: John Wiley & Sons.
Le, T. A., Huynh, Q. T., Nguyen, T. H., Nguyen, N. H., & Cao, P. N. (2020, 28-29 Aug. 2020). A method for project completion cost predicting using lstm in earned value management technique. Paper presented at the 2020 4th International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom).
Levitt, R. E. (2011). Towards project management 2.0. Engineering Project Organization Journal, 1(3), 197-210. https://doi.org/10.1080/21573727.2011.609558
Li, D. S., & Lu, M. (2017). Automated generation of work breakdown structure and project network model for earthworks project planning: A flow network-based optimization approach. Journal of Construction Engineering and Management, 143(1), 17. https://doi.org/10.1061/(asce)co.1943-7862.0001214
Liang, X. (2019). Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with bayesian optimization. Computer-Aided Civil and Infrastructure Engineering, 34(5), 415-430. https://doi.org/10.1111/mice.12425
Lipke, W. (2003). Schedule is different. The Measurable News, Summer, 31-34. https://earnedschedule.com/Docs/Schedule%20is%20Different.pdf
Liu, K. L., Cheng, J. H., & Yi, J. H. (2022). Copper price forecasted by hybrid neural network with bayesian optimization and wavelet transform. Resources Policy, 75, 10. https://doi.org/10.1016/j.resourpol.2021.102520
Liu, M., Chen, L. M., Du, X. H., Jin, L., & Shang, M. S. Activated gradients for deep neural networks. Ieee Transactions on Neural Networks and Learning Systems, 13. https://doi.org/10.1109/tnnls.2021.3106044
Louppe, G., Kumar, M., & Nahrstaedt, H. (2016). Bayesian optimization with skopt. Github. https://github.com/scikit-optimize/scikit-optimize
Moon, H., Williams, T. P., Lee, H. S., & Park, M. (2020). Predicting project cost overrun levels in bidding stage using ensemble learning. Journal of Asian Architecture and Building Engineering, 19(6), 586-599. https://doi.org/10.1080/13467581.2020.1765171
Nguyen, V., Gupta, S., Rana, S., Li, C., & Venkatesh, S. (2017). Regret for expected improvement over the best-observed value and stopping condition. Paper presented at the Asian Conference on Machine Learning.
Norman, E. S., Brotherton, S. A., & Fried, R. T. (2008). Work breakdown structures: The foundation for project management excellence: John Wiley & Sons.
Nour, M., Comert, Z., & Polat, K. (2020). A novel medical diagnosis model for covid-19 infection detection based on deep features and bayesian optimization. Applied Soft Computing, 97, 13. htttps://doi.org/10.1016/j.asoc.2020.106580
Pellerin, R., & Perrier, N. (2019). A review of methods, techniques and tools for project planning and control. International Journal of Production Research, 57(7), 2160-2178. https://doi.org/10.1080/00207543.2018.1524168
Pewdum, W., Rujirayanyong, T., & Sooksatra, V. (2009). Forecasting final budget and duration of highway construction projects. Engineering, Construction and Architectural Management. https://doi/org/10.1108/09699980911002566
Rasmussen, C. E. (2003). Gaussian processes in machine learning. Paper presented at the Summer School on Machine Learning.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.
Rozenes, S., Vitner, G., & Spraggett, S. (2006). Project control: Literature review. Project Management Journal, 37(4), 5-14. https://doi.org/10.1177/875697280603700402
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & De Freitas, N. (2015). Taking the human out of the loop: A review of bayesian optimization. Proceedings of the IEEE, 104(1), 148-175. https://doi.org/10.1109/JPROC.2015.2494218
Sharma, V., Zaki, M., Jha, K. N., & Krishnan, N. M. A. (2022). Machine learning-aided cost prediction and optimization in construction operations. Engineering Construction and Architectural Management, 29(3), 1241-1257. https://doi.org/10.1108/ecam-10-2020-0778
Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25. https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html
Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53(8), 5929-5955. https://doi.org/10.1007/s10462-020-09838-1
Vandevoorde, S., & Vanhoucke, M. (2006). A comparison of different project duration forecasting methods using earned value metrics. International Journal of Project Management, 24(4), 289-302. https://doi.org/10.1016/j.ijproman.2005.10.004
Vanhoucke, M., Coelho, J., & Batselier, J. (2016). An overview of project data for integrated project management and control. Journal of Modern Project Management, 3(3), 6-21. https://www.semanticscholar.org/paper/An-Overview-of-Project-Data-for-Integrated-Project-Vanhoucke-Coelho/e3eb1a96f475c04c40dfdb66df521ea6d7bf8cd5
Vanhoucke, M., & Vandevoorde, S. (2007). A simulation and evaluation of earned value metrics to forecast the project duration. Journal of the Operational Research Society, 58(10), 1361-1374. https://doi.org/10.1057/palgrave.jors.2602296
Verzuh, E., & Association, A. P. (2021). A guide to the project management body of knowledge: Pmbok guide.
Wauters, M., & Vanhoucke, M. (2014). Support vector machine regression for project control forecasting. Automation in Construction, 47, 92-106. https://doi/org/10.1016/j.autcon.2014.07.014
Wilmot, C. G., & Mei, B. (2005). Neural network modeling of highway construction costs. Journal of Construction Engineering and Management, 131(7), 765-771. https://doi.org/10.1061/(asce)0733-9364(2005)131:7(765)
Zhang, W. G., Wu, C. Z., Zhong, H. Y., Li, Y. Q., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on bayesian optimization. Geoscience Frontiers, 12(1), 469-477. https://doi.org/10.1016/j.gsf.2020.03.007