隨著資訊科技的急速發展和行動網路的普及，電子商務已經成為全球經濟的重要趨勢。因此市場需求逐漸轉變為「少量、多樣化」，市場需求的改變同時也提升了物流中心的作業難度。根據 De Koster et al.（2007）的研究指出，目前大多數的物流中心仍屬於勞力密集的產業，在物流中心裡與揀貨作業相關的人力佔了50%以上，相當耗成本。故導入自動化設備，並規劃一個合適的揀貨策略，將對物流中心的成本、產能以及效率有著決定性的影響。 本研究以類 LocusBots 系統為研究對象，建構一套模擬模型，探討不同倉庫分區設定與選取法則組合下之揀貨作業表現。研究情境包含分區揀貨倉庫（Type - I）與不分區揀貨倉庫（Type - II），其中分區倉庫再細分為四區、六區、九區、十二區、十八區與二十七區六種配置方式。於作業流程設計上，揀貨人員採取「先選 Block、再選 Robot」決策架構，而機器人則依不同 Zone 與 Block 選取法則進行動態路徑決策。 透過模擬實驗，比較各情境下之總系統執行時間、訂單完成時間、機器人等待時間與揀貨人員作業行為等績效指標，分析倉庫分區程度與決策法則對系統效率之影響。研究結果可作為物流中心在規劃人機協作揀貨系統、設計倉庫分區策略與決策法則時之參考依據，並有助於提升整體揀貨作業效率與系統運作效能。;With the rapid advancement of information technology and the widespread adoption of mobile networks， e-commerce has become a major trend in the global economy. Consequently， market demand has gradually shifted toward small-quantity， high-variety orders， thereby increasing the operational complexity of logistics centers. According to De Koster et al. (2007)， most logistics centers remain labor-intensive， with order-picking activities accounting for more than 50% of total labor costs. Therefore， the introduction of automated equipment and the development of appropriate picking strategies play a critical role in determining the cost， capacity， and operational efficiency of logistics centers. This study focuses on a LocusBots-like system and develops a simulation model to investigate order-picking performance under different warehouse zoning configurations and combinations of selection rules. The experimental settings include a zoned picking warehouse (Type I) and a non-zoned picking warehouse (Type II). The zoned warehouse is further divided into six configurations consisting of 4， 6， 9， 12， 18， and 27 zones. In the operational process， pickers adopt a “Block-first， Robot-second” decision structure， while robots make dynamic routing decisions according to different Zone and Block selection rules. Through simulation experiments， this study compares performance indicators across different scenarios， including Total System Time， order completion time， robot waiting time， and picker operational behavior. The effects of warehouse zoning levels and selection rules on system efficiency are then analyzed. The findings can provide practical references for logistics centers when planning human–robot collaborative picking systems and designing warehouse zoning strategies and selection rules， thereby improving overall picking efficiency and system performance.
