根據工研院IEK統計資料顯示，2019年全球無人搬運車達到18萬台，其中最主要的原因是電子商務爆炸性成長及電商大廠導入大量物流機器人，又以無人搬運車為大宗，連帶影響全球倉儲物流自動化的進程，Amazon的Kiva無人搬運車每年位公司節省9億美元的人力開支，減少每件商品之物流成本21.3美分，占總物流成本48%；因此許多行業均對AGV(Automated Guided Vehicle)進行深入研究與開發以獲取更多利潤，然而AGV系統屬於離散事件動態系統，在優化與分析的過程中，需要考慮的因素很多，例如 : AGV車輛數、AGV揀貨路徑、AGV貨載量等等，若使用數學模型求解，過程將會極度複雜與耗費時間，本研究利用系統模擬方法來探討AGV相關規劃問題，以貨到人的揀貨模式，AGV小車不斷往返貨架與揀選台之間的情境，優化AGV車輛數與最佳揀貨量為目標，利用Plant Simulation 建立情境模型，為物流中心輕型料架搬運車提供最適化之規劃。

According to IEK statistics of the Industrial Technology Research Institute, the number of Automated Guided Vehicles in the world have reached 180,000 in 2019. The main reason is the explosive growth of e-commerce and the introduction of a large number of logistics robots by major e-commerce factories. The process of global warehousing and logistics automation with Amazon’s Kiva AGVs save US $ 900 million in labor costs per year and reduce the logistics cost of each product by 21.3 cents that accounts for 48% of the total logistics cost. Therefore, many industries have more interests on research of AGVs to obtain more profits. But the AGV system is a discrete event dynamic system. In the AGV planning process of optimization and analysis, there are many factors that need to be considered, such as AGV vehicles, AGV picking path and AGV cargo load. If applying a mathematical model to solve, the process will be extremely complicated and time-consuming. This research employs system simulation to solve the AGV planning problem. Consider the picking mode, the goods are delivered by Movable Racks Vehicles to picking man with the vehicles continuously travelling between the shelf and the picking station. The Plant Simulation software is applied to establish this model to provide an optimal decision plan for AGV planning.