近年來，自動化倉儲系統(Automated Storage and Retrieval System, AS/RS)解決了傳統倉儲系統土地資源浪費、作業速度較慢與儲存成本較高的問題。因自動化倉儲需要快速地、便捷地與準確地執行倉儲管理與控制作業，故其最佳化的搬運策略被視為重要的效能指標。
傳統的自動化倉儲都是由單深式(Single-Deep)的儲貨架所組成，且由存取機Storage and Retrieval (S/R) Machine於該貨架巷道中間的路徑進行運動控制。然而，在一個廠房範圍有限的工廠，如果我們採用雙深式(Double-Deep)自動化倉儲它將可以儲存更多的貨物。文獻回顧指出雙深式自動化倉儲中最佳化排程(Optimal Scheduling)的搬運方式在現代工業化生產中所使用的彈性製造系統(FMS)是非常重要的一環。
本論文應用蟻群演算法(ACO)於雙深式自動化倉儲的最佳化排程。目標是為了得到最小的搬運工作距離，換句話說，也使得S/R Machine有了最短的搬運時間。經由模擬的結果與比較，顯示出研究所提出的方法具有優點與可行性。

In recent years, the Automated Storage and Retrieval Systems (AS/RS) have solved the problems associated with traditional warehouse systems, such as waste of land resources, slow operation speed, and high storage cost. Due to the requirements of AS/RS include the rapid, convenient, and accurate execution of warehouse management and control, the optimal scheduling of retrieval jobs is regarded as an important performance index in the systems.

Traditional AS/RS are almost structured in the single-deep configuration for the storage rack, and the Storage and Retrieval (S/R) machine processes movement control in the middle of the pathway. However, in a range-limited factory, it will store significantly more goods if we adopt the double-deep AS/RS. Literature survey indicates that we have investigated the optimal scheduling of retrieval jobs for double-deep AS/RS is very important for the Flexible Manufacturing System (FMS) used in modern industrial production.

In this paper, Ant Colony Optimization (ACO) is applied to optimal retrieval scheduling in double-deep AS/RS. The objective is to minimize the working distance, i.e., the shortest retrieval time travelled by the S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

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