摘要
建築專案開工前，此階段擁有資訊較少，對於現場管理人力配置人數多由人均產能依專案預算來預估，雖能簡單快速推估，其精準度卻很差。本研究蒐集國內外現場管理人力預測相關文獻，彙整出現場管理人力配置的影響因素，並且利用專家問卷、模糊偏好關係(FPR)及統計分析工具(SPSS)篩選出在建築專案開工前顯著影響現場管理人力配置的因子，接著應用生物共生演算法最小平方差支持向量機(Symbiotic Organisms Search-Least Squares Support Vector Machines, SOS-LSSVM)建立專案開工前現場管理人力配置推論模式，藉由模式訓練與測試，找出輸入(影響因子)與輸出(管理人數)的映射關係，做出合理的現場管理人力配置推論。本研究透過文獻彙整、專家問卷調查與統計分析工具篩選出8個影響因子，接著根據此8個因子蒐集43筆實際工程案例並建立案例庫。將案例資料隨機分成10組，利用交叉驗證準則(Cross Validation)的概念進行訓練與測試。推論結果顯示平均絕對百分比誤差(Mean Absolute Percent Error, MAPE)值界於10~30%，屬於合理且接近優良的推論，有效取代傳統主觀經驗之預測。最後將SOS-LSSVM與其他推論模式相比較，其結果亦優於迴歸分析(Regression)、倒傳類神經(BPNN)、支持向量機(SVM)、最小平方差支持向量機(LS-SVM)、演化式支持向量機推論模式(ESIM)與演化式最小平差支持向量機(ELSIM)，表示本研究應用SOS-LSSVM模式更能有效且準確地做出推論。
關鍵詞：生物共生演算法最小平方差支持向量機（SOS-LSSVM）、現場管理人力配置推論、模糊偏好關係(FPR)

Abstract
It is common in most construction projects for building that manpower plans of on-site engineer, due to insufficient information, are usually developed based on the average productivity or project budget for each function. Although this approach can be done quickly, but it rarely delivers good accuracy.
This research started with reviewing domestic and international papers about manpower of on-site engineer estimation, and aggregating factors that affected manpower plans of on-site engineer. We followed to use results from expert survey, fuzzy preference relationship (FPR) and statistical analysis tool (SPSS) to select the prominent factors for manpower plans of on-site engineer prior to the beginning of the construction projects for building. The technique of symbiotic organisms search-least squares support vector machines (SOS-LSSVM) was then deployed to set up an inference model, which, through trial and test, could be further used in mapping out the relation between input (effect factors) and output (manpower of on-site engineer requirements), and generating the most reasonable manpower of on-site engineer prediction model.
Eight factors were generated from the paper review, expert survey, and statistical analyses. Forty three construction projects for building were identified, guided by these 8 factors, as the study database. Data from these projects were separated into 10 groups randomly, and trialed and tested by using cross validation principles. The results rendered a mean absolute percent error (MAPE) in the range of 10%~30%, which indicated the methodology to be reasonable and close to excellent, and justified itself to replace the traditional prediction by personal experiences.
This SOS-LSSVM based approach was further compared to other inference models, such as Regression, BPNN, SVN, LS-SVM, ESIM and ELSIM, all resulted in favor of our methodology. We therefore concluded that it is more effective and precise to use SOS-LSSVM model in manpower of on-site engineer planning.

Key words: Symbiotic Organisms Search-Least Squares Support Vector Machines (SOS-LSSVM), manpower of on-site engineer prediction model, Fuzzy Preference Relationship (FPR)

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