一般設計專案之作業項目乃依據專案目標及業主需求進行解析而得，而其工作內容大多是在處理資訊，但由於作業所需之資訊內容具有不確定性，因此設計作業往往需透過反覆資訊傳遞方能得到一合適的設計結果，而反覆設計之範圍可能橫跨多個設計作業項目，形成一個或數個反覆執行迴圈，迴圈中作業項目愈多，愈易導致設計作業的缺失。由於設計作業間之關係大多取決於資訊的流向，因此有必要從資訊流解析作業項目並尋求最佳之設計作業程序。
　　過去相關研究提出以建立設計作業項目間之相依關係結構矩陣，來處理作業項目間反覆設計之問題，其結果顯示良好的設計作業程序將提升作業項目間資訊傳遞之流暢性，減少猜測資訊所造成的作業錯誤及延宕，因而大幅改善整體設計專案之執行結果。然而，過去在設計作業程序規劃之研究對於資訊流傳遞之可能影響程度考量尚有缺失，使得評估結果易產生偏差，此外，應用相依關係結構矩陣於作業程序規劃輔助時，其建構之矩陣規模乃隨著作業項目的數量之增加而成長，因而在處理複雜專案所衍生的數量龐大的作業項目程序規劃上，亦容易產生規劃時間過長而導致運作效率不佳之問題。
　　因此本研究針對上述程序規劃準則及規劃效率之問題，發展一套以資訊流傳遞行為為基礎之作業程序評估方法，以確切評量各條資訊流對於設計作業程序所產生的不同程度的影響，並在促進規劃效率方面，應用快速混雜基因演算法之於大型排列組合問題之求解能力，建構為一完整之設計作業程序規劃之最佳化模式，以期有效搜尋專案之各種作業程序組合中，具有最佳資訊流傳遞性的作業程序，從而降低資訊傳遞不佳而導致之作業失敗機率及成本與時間之浪費。
　　經由案例之測試及驗證結果顯示本研究開發之最佳設計作業程序規劃模式，在結合以資訊流傳遞行為為基礎所建立之作業程序評估方法及快速混雜基因演算法之最佳化工具後，確可規劃出與過去研究規劃結果相同甚或更佳之設計作業程序，並於合理時間內有效尋得最佳解，以提供管理者於處理設計作業程序規劃問題時之參考。

　The design project is typically involved with processing a large amount of information from various participants. However, owing to the uncertainty within the information, engineers have to repeatedly perform design activities, such as exchange information with other participants or process information, to produce an appropriate result. This persistent information exchanging process may involve many activities and come into one or several iterative design loops. As there are more activities involving within the iteration loop, the productivity of the design project could fall down easily. Furthermore, because the relationships among the design activities are mainly determined by information flows, the sequence of performing design activities should be properly planned to avoid unnecessary iteration loops. Therefore, it is essential to develop a model that can improve the productivity of the design phase by reducing the unnecessary iterations loops.
　Dependency structure matrix (DSM) is one of the widely used methods to describe the design process. By employing DSM, the relationships and the sequence of the design activities can be identified. Many researchers had developed various models to optimize the design process according to DSM. However, the impact of iteration loops within the design process is not thoroughly considered in these studies. In addition, as the number of activities within DSM increases, the optimization process becomes computationally inefficient. Therefore, it is necessary to build a more efficient model which not only thoroughly consider the impact of iteration loops but also find the optimal design process for the large-size design project.
　In this research, new principles of evaluating the design process are developed. Furthermore, fast messy genetic algorithm (fmGA) is employed along with the newly developed evaluation principles to build the optimization model of the design process. Results showed that, based on the proposed evaluation principles, the impact of the iteration loops can be clearly identified. In addition, with the proposed optimization model and its computer implementation, engineers can find the optimal design process for the large-scale design project efficiently.

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