在現代生產環境中，各種不確定因素往往導致產線中斷，機器故障的頻繁發生更加重了維護工作的重要性。本論文聚焦於如何在工廠環境中即時有效地對機械故障進行糾正性維護，並策略性地設立服務站點(維護站)以及人力資源分配。首先，透過工廠已知的機器位置和操作員技能作為背景，以減少對機械故障的反應時間並最小化人力成本為目標，建立一個多目標優化問題。在此問題中，維護站的位置以及該站的人力配置是影響維護時間及人力成本的重要考慮因子。本研究運用了非支配排序遺傳演算法 (Non-dominated sorting genetic algorithm, NSGAIII)多目標優化模型來求解此決策問題，並將具有不同技能的操作員策略性地分配到這些站點。操作員的分配旨在確保對機械故障的快速反應並加快維修過程，同時也要最小化人力成本以提高資源利用率。本文在各種機器故障頻率的場景下檢驗我們解決方案的可行性和有效性。結果顯示，本研究提出的解決方案能夠在不同機器故障場景下在反應時間和人力成本之間保持平衡。此外，實驗結果顯示本研究提出的方法與工廠現行使用的方法表現更好，提供了更有效的解決方案。

In modern production environments, various uncertainties often lead to production line disruptions, and the frequent occurrence of machine failures emphasizes the importance of maintenance work. This thesis focuses on how to carry out corrective maintenance on machine failures in a factory environment in a timely and effective manner, and strategically establish service points (maintenance stations) and allocate human resources. Firstly, using the known locations of factory machines and the skills of the operators as background information, a multi-objective optimization problem was formulated with the goal of reducing the response time to machine faults and minimizing labor costs. In this problem, the location of the maintenance station and the operator of the station are important considerations that affect maintenance time and labor costs. The Non-dominated sorting genetic algorithm (NSGA III) multi-objective optimization model is used in this study to solve the decision problem and to strategically allocate operators with different skills to these stations. The allocation of operators is aimed at ensuring a rapid response to machine faults and speeding up the repair process, while also minimizing labor costs to improve resource utilization. The feasibility and effectiveness of the solutions proposed in this thesis are examined under
various scenarios of machine failure frequency. The results show that the proposed solutions can balance response time and labor costs under different machine failure scenarios. Furthermore, experimental results show that the methods proposed in this study perform better than the methods currently used in factories, providing a more effective solution.

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