施工過程中，受限於環境、天氣等眾多因素影響，造成完工工期經常難以準確掌控，施工單位在預測工期時，必須仰賴過去之經驗，無法即時反映影響工期之因素並利用工程現況客觀地預測完工工期。而本研究之專案實獲完工工期推論模式是基於專案的實獲值管理(Earned Value Management, EVM)績效與影響工期的因子對專案的工期進行預測，可作為一個預警參數，經由準確的預測結果，專案管理者可以針對時程採取適宜的管理，設法改善並避免問題的發生。
本研究透過案例學習發展建立專案實獲完工工期推論模式，首先整理相關文獻，並利用影響圖和因子篩選表來彙整工期影響因子，再結合實獲值管理和實獲時程法(Earned Schedule Method, ESM)之績效指標，確立輸入變數並建立案例資料庫，作為完工工期推論模式之基礎。接著應用 Symbiotic Organisms Search - Least Squares Support Machine(SOS-LSSVM)[1]為推論模式之核心，學習歷史案例，找出每期輸入變數與待完工成本之間的映射關係，進而計算預估完工工期(Estimate Schedule At Completion, ESAC)。藉此作為施工過程中作為時程管控的參考依據，以達到提前預警的目的。
本研究之專案完工工期推論模式，其預測結果為高精確、高相關且變異性小而穩定，經由結果比較後，證實可提供較其他人工智慧模式或實獲值公式更佳的預測準確率，以協助管理者進行時程之管控。

Due to the Restriction by many factors like the environment, weather, and etc., it is difficult to control the schedule accurately during the execution phase. Contractors rely on the past experience to predict the duration which cannot reflect the influence factors and use the current situation in time. In this research, the Earned Schedule Inference Model used the earned value management (EVM) and influence factors of schedule to predict the duration for construction project. Through a precise prediction of project duration, the project manager can take control of schedule in order to avoid problems.
This research established the Earned Schedule Inference Model by learning the historical cases. First, the related references was collected and then the influence diagram and the filter diagram were used to figure out the influence factors of project duration. The next step is to combine them with the index of EVM and Earned Schedule Method (ESM). Furthermore, the input and the output variables were identified to establish the database of historical cases which is the base of the model. Second, the Symbiotic Organisms Search - Least Squares Support Machine (SOS-LSSVM) was applied to the model as its main core in order to discover the relationship between the input (influence factors and EVM) , and the output variables (Estimate Schedule to Completion, ESTC) which can be used to calculate to the Estimate Schedule at Completion (ESAC). With the established duration project model, the manager can control the schedule during the execution phase.
The obtained results show that this research can provide a highly accurate and stable forecasting. By comparing the results with those of the other Artificial Intelligence (AI) methods and formulas of EVM, it is confirmed that the Earned Schedule Inference Model has a better ability of predicting the duration in order to help schedule control.

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