自動化搬運系統在工廠自動化中扮演著非常重要的角色，而其中之車數決策問題更為導入此系統時之關鍵議題之一。然而，有鑑於自動化搬運系統與製程之複雜性與不確定性〈如：機台之加工時間〉，此系統之車數決策問題已屬不易，若再納入多個績效目標同時考量之情況下，將使問題更趨複雜。本研究係針對此自動化搬運系統中之車數決策問題，提出一整合性之方法，即結合模擬最佳化及資料包絡分析法以求得在多個績效目標下之最佳車數配置。由於利用模擬方式所得之績效指標值近似於隨機環境中之績效指標實際值，因此在第二階段利用資料包絡分析法時，所求得之車數配置方案之績效分數即會非常接近實際情況。最後，利用一實例以驗證所提出方法在實務問題中之有效性及可行性。

Automated Material Handling System (AMHS) plays a key role in factory automation. Vehicle feet sizing is one of the critical issues when designing an effective AMHS. However, due to complexity of AMHS design and uncertainty involved in the production process, e.g., random processing time, vehicle feet sizing is a challenging problem, especially when there are multi-objectives, e.g., minimized cycle times and maximized throughputs are simultaneously desired. In this paper, we propose a novel framework which integrates simulation optimization techniques and Data Envelopment Analysis (DEA) to facilitate the identification of the optimal feet sizes of AMHS under multiple objectives. The trade-os between different objectives can also be demonstrated. A numerical study shows that the proposed framework can outperform the traditional approaches. In addition, an empirical study at the end verifies the effectiveness and the viability of the proposed framework in practical settings.

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