目前營建工程 管理人力的配置多是依據人均產能、公司政策、經驗主觀判斷以及專案預算來預估所需管理人力。 但營建工程 隨 著專案的進行管理人力 的需求亦隨之變動 。 目前 傳統 預估管理人力配置的方式，無法適時合理地反映工程現場需要的管理人力數量 。 造成專案管理人力規劃過剩或不足的問題。 本研究導入 實獲值管理 (Earned Value Management, EVM)的績效與指標 有效反映工程項目與工程動員數之多寡，因此能在工程專案進行期間，能適時合理地反映因施工動態與 現場 作業人機料數量變動的管理人力需求。本研究藉由實務案例蒐集彙整分析23件 建築工程 案例中逐月紀錄共867筆歷史資料 篩選過往文獻中的管理人力因子轉換為 實獲值管理的參數與指標 並結合 人工智慧模式 建構營建專案管理人力推論模式， 其結果符合 高準確度預測， 適時客觀地反映管理人力需求，解決傳統依人均產能、公司政策、經驗主觀判斷及專案預算來規劃管理人力造成管理人力配置不適當的問題 ，可作為模式引用者參考。

The allocation of construction engineer resource is often based on per capita capacity, company policy, subjective judgement and project budgets.However, as a project progresses, construction engineer resource requirements change.The current traditional way of estimating allocation of construction engineer resource does not reflect the allocation of construction engineer resource required on site in a timely and reasonable manner.This has led to the problem of over or under planning of project construction engineer resource.This study introduces EVM performance and metrics to effectively reflect the number of project and project workforce.This will enable the project to reflect the changes in construction dynamics and the number of personnel and materials on site in a timely and reasonable manner during the project.
A total of 867 monthly historical records from 23 construction projects were collected and analysed through practical case studies.The allocation of construction engineer resource factors from the past literature were selected and converted into EVM variables and indicators.The model was combined with an artificial intelligence model to develop a construction engineer resource inference model for construction project management.The model results are consistent with highly accurate predictions.The results are highly accurate and reflect the management workforce requirements in a timely and objective manner.The problem of inappropriate management workforce allocation based on per capita capacity, company policy, subjective experience and project budgets has been resolved.The results of this study can be used as a reference for model users.

[1] 温亦瑄, "工程管理費預算編列參數模式之研究," 營建管理研究所, 國立中央大學, 2013. [Online]. Available: http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=100325003
[2] 林家豪, "營造廠現場管理人力配置之研究," 碩士, 營建工程系, 國立臺灣科技大學, 台北市, 2003. [Online]. Available: https://hdl.handle.net/11296/s74tp2
[3] 莊秉元, "工地管理人力規劃之研究－以學校建築工程為例," 碩士, 營建工程所, 國立高雄第一科技大學, 高雄市, 2005. [Online]. Available: https://hdl.handle.net/11296/nxg8w6
[4] 黃明東, "建築專案現場管理人力配置推論模式之建立," 碩士, 營建工程系, 國立臺灣科技大學, 台北市, 2017. [Online]. Available: https://hdl.handle.net/11296/qhpsjb
[5] M.-C. Chien, "營建工程人力調派最佳化決策模式之研究," National Central University, 2005.
[6] 李宗憲, "實獲值管理在工程專案管理之應用改進研究," 2008.
[7] W. L. Price, A. Martel, and K. Lewis, "A review of mathematical models in human resource planning," Omega, vol. 8, no. 6, pp. 639-645, 1980.
[8] 張志國, "營造工地管理人資量化與預測," 碩士, 土木工程學系碩士在職專班, 國立中央大學, 桃園縣, 2005. [Online]. Available: https://hdl.handle.net/11296/k6w9a5
[9] 陳韋向, "營建專案人力配置最佳化及參與時序模式建立," 博士, 營建管理研究所, 國立中央大學, 桃園縣, 2014. [Online]. Available: https://hdl.handle.net/11296/6b88jm
[10] 俞偉武, "營建工程短期人力指派最佳化模式之研究," 碩士, 土木工程學系碩士在職專班, 國立中央大學, 桃園縣, 2008. [Online]. Available: https://hdl.handle.net/11296/877e7a
[11] 簡明智, "營建工程人力調派最佳化決策模式之研究," 碩士, 土木工程學系碩士在職專班, 國立中央大學, 桃園縣, 2005. [Online]. Available: https://hdl.handle.net/11296/r2d82c
[12] A. L Agarwal, B. Rajput, and E. Mangesh A, "Model formulation to estimate manpower demand for the real-estate construction projects in India," Organization, technology & management in construction: an international journal, vol. 5, no. 2, pp. 828-833, 2013.
[13] R. T. Hazem and P. Adavi, "INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY IMPACT OF
EXTERNAL AND HUMAN FACTORS ON LABOR PRODUCTIVITY OF CONSTRUCTION PROJECTS IN IRAQ."
[14] A. Enshassi, S. Mohamed, and S. Abushaban, "Factors affecting the performance of construction projects in the Gaza strip," Journal of Civil engineering and Management, vol. 15, no. 3, pp. 269-280, 2009.
[15] M. R. Hosseini, N. Chileshe, P. Ghoddousi, and O. Poorafshar, "Investigating Projects' Working Environment Effects on Labour Productivity: Perceptions of Iranian Road Contractors' Managers," Association of Engineering, Project, and Production Management, 2013.
[16] B. Chigara and T. Moyo, "Factors affecting labor productivity on building projects in Zimbabwe," International Journal of Architecture, Engineering and Construction, vol. 3, no. 1, pp. 57-65, 2014.
[17] N. Valverde-Gascueña, E. Navarro-Astor, J. Fuentes-del-Burgo, and J. P. Ruiz-Fernandez, "Factors that affect the productivity of construction projects in small and medium companies: Analysis of its impact on planning," Management, vol. 879, p. 888, 2010.
[18] K. H. Rose, "A guide to the project management body of knowledge (PMBOK® Guide)—Fifth Edition," Project management journal, vol. 3, no. 44, pp. e1-e1, 2013.
[19] 吳建燁, "專案實獲完工工期推論模式之研究," 碩士, 營建工程系, 國立臺灣科技大學, 台北市, 2016. [Online]. Available: https://hdl.handle.net/11296/zq9h3j
[20] E. F. Codd, "Normalized data base structure: A brief tutorial," in Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control, 1971, pp. 1-17.
[21] S. Geisser, "The predictive sample reuse method with applications," Journal of the American statistical Association, vol. 70, no. 350, pp. 320-328, 1975.
[22] J. D. Rodriguez, A. Perez, and J. A. Lozano, "Sensitivity analysis of k-fold cross validation in prediction error estimation," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 3, pp. 569-575, 2009.
[23] M.-Y. Cheng and D. Prayogo, "Symbiotic organisms search: a new metaheuristic optimization algorithm," Computers & Structures, vol. 139, pp. 98-112, 2014.
[24] J. A. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural processing letters, vol. 9, no. 3, pp. 293-300, 1999.
[25] A. De Myttenaere, B. Golden, B. Le Grand, and F. Rossi, "Mean absolute percentage error for regression models," Neurocomputing, vol. 192, pp. 38-48, 2016.
[26] R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," International journal of forecasting, vol. 22, no. 4, pp. 679-688, 2006.
[27] B. Yegnanarayana, Artificial neural networks. PHI Learning Pvt. Ltd., 2009.
[28] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
[29] H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines," Advances in neural information processing systems, vol. 9, pp. 155-161, 1997.
[30] D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression. John Wiley & Sons, 2013.
[31] R. J. Lewis, "An introduction to classification and regression tree (CART) analysis," in Annual meeting of the society for academic emergency medicine in San Francisco, California, 2000, vol. 14: Citeseer.
[32] M.-Y. Cheng and Y.-W. Wu, "Evolutionary support vector machine inference system for construction management," Automation in Construction, vol. 18, no. 5, pp. 597-604, 2009.