營建工程之成敗可以以工期、成本和品質三項因子作為衡量。由於工程的品質標準於合約簽訂時已經明確規範，所以如何規劃工程及控制成本就成為一個專案工程中的重點。

營建工程之中，各作業時間及成本之間可能存在相關性，如作業時間延遲而造成成本增加，因此忽略作業時間與成本之相關性，可能會導致錯誤的評估。因此必須將作業之間的相關性進行量化，作為評估之依據，在確定作業之間的相關性及相關係數後，再使用相關性模擬NORmal To Anything (NORTA)及Iman and Conover(IC)來進行成本預估與工期排程。但使用NORTA及IC進行相關性模擬時必須使用Cholesky分解，但如果遭遇原始相關係數矩陣為非正定特徵值為負的情況下，Cholesky分解後會產生虛數導致相關性模擬無法繼續進行下去，雖然已經有學者提出將矩陣修正為正定之方法，但經過正定化的矩陣將會偏離原本相關係數矩陣，造成成本及工期時間的預估錯誤。

因此，本研究將利用質群演算法搜尋一個正定且近似原始之相關係數矩陣，藉此校正因正定化矩陣所產生的誤差。另外，由於無論是演算法或者相關性模擬都必須耗費大量的時間進行運算，因此本研究也將使用電腦叢集，控制計算核心之溝通開發平行計算策略，以降低系統計算時間。於最後並將本研究所提出之架構應用於實務案例之上，以瞭解其應用可能。其結果驗證本研究之架構可有效校正誤差。

The success of a construction project can be determined by aspects of its scheduling , costs and quality. Its quality standards have already been specified the moment the contract is signed. Therefore, how to plan the schedule and control the costs have become an important issue in project management.
In a construction project individual operations often have some correlation with one another. For example, the delay in operation time will cause the costs to increase.However, when material cost increases,it influences the costs of all the related activities. Therefore, if the correlation between time and cost is ignored, there is a possibility in giving a inaccurate evaluation.
It is necessary to quantify the correlations between operations to obtain more accurate estimation. fter the correlations are determined,they can be used o conduct simulations through NORmal To Anything (NORTA) and Iman and Conover(IC) to develop a cost estimation or schedule prediction.During the process,Cholesky factorization is needed to conduct a correlation simulation.But in situations when the original correlation matrix is not positive definite,the Cholesky factorization will generate imaginary roots,thus causing the simulation to fail.Evan thought many scholars have already presented methods to adjust the correlation matrix,the modified matrix causes inaccuracy is cost estimale and schedule prediction.
Therefore, this study uses particl swarm optimizstion (PSO)to search for a feasible correlation matrix, which after correlation simulation will lead to minimum error.Since PSO and cooelation simulation are both computational expensive,this study investigates the use of a computer cluster and three parallel progrmming strategies in reducing computatial time.

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