隨著電子商務快速發展與倉儲自動化技術日益成熟，物流中心在揀貨作業上正面臨訂單型態高度碎片化與作業負荷不均等挑戰。尤其在自主型移動機器人（Autonomous MobileRobots， AMR），如Kiva和Lucusobots系統導入後，人機協作的揀貨流程設計與資源調度策略，已成為智慧物流系統中影響倉儲效率的重要議題。 本研究以分區式倉儲環境為研究情境，系統性探討人機協作揀貨系統中不同作業流程設計（Type-I 與 Type-II）、資源配置方式（Picker與Robot）以及揀貨人員決策行為（Block選取及Robot選取問題）之交互影響。Type-I流程為揀貨人員在傳統固定區域（Fixed Zone）下，揀貨人員僅能於其所屬區域（Zone）內執行揀貨作業；而Type-II流程中，則引入以揀貨人員為中心之動態區域（Dynamic Zone）概念，使揀貨人員之可服務範圍可隨揀貨人員作業狀態動態調整，以提升作業彈性。 透過多因子變異數分析與模擬實驗，本研究以系統總執行時間（Total System Time， TST）與平均批次化訂單在機器人上時間（Avg Batch Trip Time）作為主要績效指標，分析不同作業流程與決策法則組合對整體系統效能之影響。研究結果可作為物流中心在揀貨流程設計與人機資源配置決策上的參考依據，並為未來智慧物流與人機協作系統之研究奠定基礎。;With the rapid development of E-commerce and the increasing maturity of automated warehousing technologies， logistics centers are facing significant challenges in order picking， such as highly fragmented order patterns and uneven workloads. Particularly with the introduction of Autonomous MobileRobots (AMRs)， such as Kiva and Locusbots systems， the design of human-robot collaborative picking workflows and resource scheduling strategies has become a critical issue affecting the efficiency of smart logistics systems. This study focuses on a zoned warehousing environment to systematically investigate the interactive effects of different workflow designs (Type-I and Type-II)， resource allocation methods (Pickers and Robots)， and picker decision-making behaviors (Block selection and Robot selection problems) within a human-robot collaborative picking system. In theType-I workflow， pickers operate within traditional Fixed Zones， restricted to performing tasks only within their assigned areas. In contrast， the Type-II workflow introduces the concept of Dynamic Zones， where the service range of a picker can be adjusted dynamically based on their operational status to enhance operational flexibility. Through multi-factor Analysis of Variance (ANOVA) and simulation experiments， this research utilizes Total System Time (TST) and Avg Batch Trip Time as primary performance indicators to analyze the impact of various combinations of workflows and decision rules on overall system efficiency. The findings provide a practical reference for logistics centers in designing picking processes and making resource allocation decisions， while establishing a foundation for future research in smart logistics and human-robot collaborative systems.
