工業 4.0 是近年來政府積極推動的工業升級計劃。它的主要概念基於虛實整
合系統（CPS），並應用了各種資訊技術，例如大數據、物聯網、自主機器人，模
擬等，主要目標是將上述技術緊密連結，整合創造一虛實整合製造系統，實現「智
慧工廠」的終極目標。其中，模擬在工業 4.0 在當中扮演了一個重要的腳色。目
前，它主要用於高科技工廠、食品加工與汽車裝配製造等自動化與標準化程度較
高的產業，在營建產業製造方面的應用仍然較少。
在營建行業中，鋼結構工廠的產品具有標準化的製造流程。其中，BOX 柱
一次加工區是一具有標準制造流程的生產線。在柱一次加工的生產過程中，相較
標準化程度較高的如半導體產業以及食品加工業，鋼構件存在每一個產品不一樣
且生產過程較為複雜的特性，例如構件的長度，剪力釘的數量等都會影響桿件的
製造時間，而每一支桿件生產程序也可能會因為桿件的不同而有些微差異。此外，
現場的工作效率很大程度上取決於人力的配置，工作站中的工人數量將會影響其
製造效率。
這項研究首先旨在為 BOX 柱一次生產線的運行開發一個模擬模型。根據實
際生產線運作，分析簡化後，使用離散事件模擬對每個工作站進行參數配置和建
模，構成一條虛擬生產線。 以 Python 和 Simpy 建立模擬模型後，本研究進一步
採用基因演算法（GA）來找到兩種應用場景中的最佳模擬結果，以支持生產管
理決策。最後，本研究還使用 Spark 平行運算對演算法進行加速，使其在欉集平
台上運行，以加快計算時間。

Industrial 4.0 is the industrial upgrading program which is actively promoted by the government in recent years. Its main concept is based on the Cyber-Physical System (CPS) with the applications of various information technologies, such as big data, IoT autonomous robots, simulation, etc. The main goal is to achieve ”smart manufacturing”, and the above-mentioned technologies are closely linked to create a cyber-physical industry. Among them, simulation plays an important role in Industry 4.0. Currently, it’s mostly used in high-tech factories, and its applications in the construction industry are still rare.
In the construction industry, the productions of steel structure factory have the characteristics of standardization, mass production, and customization. Among them, the BOX column processing is a production line with standard operation flow. However, in the current situation, the component production process is complicated and each component has inconsistent characteristics. For example, the length of the component, the number of shear stud, etc. In addition, the operations in the field highly depends on the manpower. The number of workers in a working station may affect its efficiency. This study first aims to develop a simulation model for the operations of the BOX column production line. Based on the actual operations and the resources available on the production line, the parameter configurations and the simulation logics are carried out for each working station to form the virtual production line using discrete events simulation. A simulation engine is built based on the proposed simulation model using Python and Simpy After that, this study further adopt genetic algorithm (GA) to find optimal simulation results for two application scenarios to support decision making on production management. Finally, this study also implements the optimization algorithm using Spark to run on a cluster platform for speeding up the computing time.

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