隨著資訊科技與行動通訊技術的快速發展，電子商務產業持續成長，加上新冠肺炎疫情的影響，消費者購物行為大幅轉向線上通路，使物流中心在營運管理上，不僅需面對訂單量提升，亦須因應少量多樣化訂單型態日益普及所造成的挑戰。在物流中心各項作業流程中，揀貨作業不僅為最耗費時間之環節，亦為人力成本占比最高的作業項目，其效率高低將直接影響整體訂單處理能力與服務水準。因此，如何有效改善揀貨流程並提升作業效率，已成為智慧物流系統發展的重要研究議題。 本研究以類LocusBots系統之揀貨作業環境為研究對象，探討揀貨人員在揀貨流程中進行Block選取與Robot選取之決策問題。研究方法採用模擬分析方式，建立分區揀貨作業模型，並設計多種Block選取法則與Robot選取法則進行實驗比較，並以總系統時間、平均訂單延遲時間、平均批次訂單旅行時間與平均批次訂單週期時間等指標進行分析與比較，分析不同策略組合對揀貨時間與整體作業效率之影響。研究結果期望能找出具備較佳績效表現之揀貨策略組合，以降低揀貨作業時間、提升系統效率，並為智慧物流揀貨系統之設計與實務應用提供具體參考依據。研究結果將可為物流中心的揀貨決策提供參考依據，並為未來相關研究提供實質性的貢獻。;With the rapid development of information technology and mobile communication， the e-commerce industry has experienced continuous growth. Driven further by the COVID-19 pandemic， consumer shopping behavior has significantly shifted toward online channels. Consequently， distribution centers now face the dual challenge of managing surged order volumes and adapting to the increasingly prevalent "small-volume， high-variety" order patterns. Among the various operational processes within a distribution center， order picking is not only the most time-consuming phase but also the most labor-intensive component. Therefore， how to effectively optimize the picking process and enhance operational efficiency has become a critical research topic in the development of smart logistics systems. This study focuses on an order-picking environment utilizing a pseudo-LocusBots system to investigate the decision-making problems regarding Block selection and Robot selection by picking personnel during the picking process. A simulation analysis approach is adopted to establish a zone-picking operational model. Various block selection rules and robot selection rules are designed and experimented with for comparative analysis. Key performance indicators—including total system time， average order tardiness， average batch order trip time， and average batch order flow time—are utilized to analyze and compare the impacts of different strategy combinations on picking time and overall operational efficiency. Ultimately， by identifying the strategy combinations that yield superior performance， this study provides concrete references and actionable guidance for the design and practical application of smart logistics picking systems， offering substantial contributions to future related research.
