工業4.0透過人工智慧、雲端平台、大數據分析及觀測裝置等軟硬體新技術進行虛實整合，及時掌握分析機台與產品在生產及使用的狀況，目前許多產業正逐漸著手進行數位轉型，透過更加快速、強大的網路速度和運算能力幫助製造業進行轉型，不過就台灣傳統產業目前多為中小企業的現況，完全轉換成工業4.0的模式進行生產及運作的過程尚有許多難題必須克服，因此可透過演算法協助提高製造性能，開發出適合現有製造系統與產業模式的架構，幫助各產業升級現有的工廠，以利產業進行轉型。
以往傳統產業多依靠生管人員的人工經驗法則進行排程的方式，不只需花費高成本的人力與時間，且排程結果與實際需求產生偏差時，無論是原料不足、存貨不足或是機台閒置等原因皆會導致成本的損失，因此本研究將生產排程結合演算法的運算能力，藉此獲得更好的排程結果且節省人力與時間成本。透過改變基因演算法的編碼方式，且使用實際案例的資料也模擬各個規模的訂單數量，比較不同編碼在各個規模資料的排程結果，提供適合生產排程使用的編碼找尋所需的目標，也可以節省時間與人力成本。

Industry 4.0 uses artificial intelligence, cloud platform, big data analysis and
observation devices and other new software and hardware to integrate virtual and real,so as to grasp the production and use status of analysis machines and products in atimely manner. At present, many industries are gradually embarking on digital transformation. Faster and more powerful network speed and computing power help the manufacturing industry to transform. However, considering the current situation of Taiwan's traditional industries are mostly small and medium-sized enterprises, there are still many difficulties that must be overcome in the process of completely converting to the Industry 4.0 for production and operation. Therefore, algorithms can be used to help improve manufacturing performance, develop a framework suitable for existing manufacturing systems and industrial models, and help industries upgrade existing factories to facilitate industry transformation.

In the past, traditional industries mostly relied on the rules of thumb of production management personnel for scheduling, which not only requires high-cost manpower and time, but also when the scheduling results deviate from the actual demand, whether it is insufficient raw materials, insufficient inventory or machine tools. Idleness and other reasons will lead to cost loss. Therefore, this study combines production scheduling with the computing power of the algorithm to obtain better scheduling results and save labor and time costs. By changing the coding method of the genetic algorithm, and using the data of actual cases to simulate the number of orders of various scales, compare the scheduling results of different codes in the data of various scales, and provide codes suitable for production scheduling to find the desired targets also save time and labor costs.

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