隨著資訊科技的飛速發展及行動網路的普及，電子商務已成為全球經濟的重要發展趨勢。市場需求逐漸轉向「少量、多樣化」的模式，這一變化使物流中心的運營複雜度提高。根據De Koster et al.（2007）的研究，目前大部分物流中心仍然依賴大量人力，其中與揀貨作業相關的勞動力成本占比超過50%，導致營運成本居高不下。因此，如何透過導入自動化設備與優化揀貨策略，以降低成本並提升效率，成為企業關注的重點。 在此背景下，類Locusbots系統的運用顯示出優勢，其具備彈性調整機器人數量的能力，使其無論在訂單高峰期或淡季皆能適應變化。此外，區域揀貨方式能有效提升作業效率，讓揀貨人員僅需在指定區域內作業，無須長距離移動，大幅降低人力需求，同時提升揀貨精準度與作業速度。揀貨機器人則負責將訂單與揀貨箱運送至指定區域，使整體物流作業更加流暢。 本研究以類Locusbots系統為基礎，聚焦於機器人在分區揀貨倉庫中的運作方式，並針對其揀貨流程提出兩種主要策略：「先選擇Zone再選擇Block」與「直接選擇 Block」。研究進一步探討這兩種流程所涉及的關鍵議題，即「機器人的Zone選取問題」與「機器人的Block選取問題」。最後，透過Arena模擬軟體進行測試與數據分析，評估不同策略在各項績效指標上的影響，並比較兩種揀貨流程的優劣，期望研究成果能對未來相關議題提供參考與貢獻。;With the rapid advancement of information technology and the widespread adoption of mobile networks, e-commerce has emerged as a major trend in the global economy. Market demand has gradually shifted toward a "small-quantity, high-variety" model, increasing the operational complexity of distribution centers. According to De Koster et al. (2007), most distribution centers still heavily rely on manual labor, with labor costs related to order picking accounting for more than 50% of total operational expenses. As a result, enterprises are increasingly focused on how to reduce costs and improve efficiency through the implementation of automated equipment and the optimization of picking strategies. Against this backdrop, systems similar to LocusBots have demonstrated distinct advantages. Their ability to flexibly adjust the number of robots allows them to adapt to both peak order periods and off-seasons. Moreover, zone-based picking can significantly improve operational efficiency by limiting pickers to specific areas, thereby reducing unnecessary travel distance, lowering labor demand, and enhancing both picking accuracy and speed. The picking robots are responsible for transporting order bins to designated zones, which streamlines the overall logistics workflow. Building on the concept of LocusBots, this study focuses on the operational approach of robots in zone-based picking warehouses. Two main strategies for the picking process are proposed: (1) selecting a zone first and then selecting a block, and (2) directly selecting a block. The study further investigates the two key issues involved in these processes: the robot′s zone selection strategy and the robot′s block selection strategy. Finally, simulation experiments are conducted using Arena software to evaluate the performance of different strategies across various metrics, aiming to compare the effectiveness of the two processes and provide insights for future research in this area.
