石化產業 (Petrochemical Industry) 中的大型儲存桶槽在防止桶槽外壁與油體反應而引起鏽蝕的需求下，每年需進行檢測和塗層作業。然而，傳統的施工排程方式依賴現場工人的經驗，缺乏準確的數據化作業，導致桶槽的清洗和噴塗作業無法有效地完成。這樣的作業缺乏標準程序和排程效率，且難以精確掌握作業時間。為了克服此排程問題，結合演算法和攀附式機器人 (Wall Climbing Robot, WCR) 可以實現智動化排程的最佳化。因此，本研究旨在開發啟發式演算法，以找出最佳的作業流程，考慮到特定作業之間的優先順序和時間限制，以最小化總完工時間 (Makespan) 為目標，提供最佳的作業建議。本研究藉由攀附式機器人開發商所提供之數據來進行工序排程，考慮到桶槽屬於一整面區域，為了確保作業過程的完整性和有效性，需要明確定義排程並確保其可行性，因此，本研究將桶槽切分為多個區域，並以總完工時間最小化為目標，運用模擬退火法以及改良的模擬退火法，以找出最佳作業建議及最短總完工時間。研究結果顯示，模擬退火法及改良的模擬退火法均能找到最短總完工時間的最佳作業建議，然而，改良的模擬退火法可以在更短得時間內即獲得最佳解，因此能實際應用到石化產業中，增加效率，實現桶槽清洗噴塗作業智動化。

In the Petrochemical Industry, large oil storage tanks are subject to annual inspection and coating operations to prevent rust caused by the reaction between the tank's exterior wall and the contained substances. However, the traditional operation scheduling approach relies on the experience of on-site workers, which lacks accurate digitalization operations. As a result, the cleaning and coating operations of the oil storage tanks cannot be efficiently completed. If this scheduling problem can be combined with algorithms and wall climbing robot (WCR), the intelligence of scheduling can be realized. Therefore, this study aims to develop a metaheuristics algorithm to determine the optimal workflow, considering the prioritization and time constraints, to minimize makespan. In this study, all the data are provided by the wall climbing robot developer. Considering that the oil storage tank constitutes a continuous surface area, to ensure the integrity and effectiveness of the operations. Therefore, this study divides the tank into multiple blocks and aims to minimize makespan. The original simulated annealing and the improved simulated annealing are employed to find the optimal operational recommendations and minimum makespan. The results show that both the original simulated annealing and improved simulated annealing can be conducted to find the optimal operational recommendations and minimum makespan. However, the improved simulated annealing can obtain the optimal solution in a shorter computation time. Therefore, it can be practically applied to real-life scheduling.

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