| 摘要: | 隨著電子商務快速發展與倉儲自動化技術成熟，物流中心面臨愈趨複雜的揀貨挑戰。尤其在協作型移動機器人（如 LocusBots）導入後，人機協作的揀貨流程設計成為倉儲優化核心課題。本研究以分區式倉儲環境為情境，系統性探討作業流程設計（Type-I 與 Type-II）、資源配置（Robot 數量, RN）、與揀貨人員決策策略（Block 選取法則 PB 與 Robot 選取法則 PR）之交互影響。Type-I 流程為揀貨員先選擇區塊 (Block) 後選 Robot，Type-II 流程則跳過區塊選擇，直接由整體可用 Robot 中挑選。
透過大規模模擬實驗與多因子變異數分析，本研究針對系統總執行時間（Total System Time, TST）與訂單在系統內總時間（Total Time In System, TTIS）兩項績效指標進行系統性探討。結果顯示，資源配置與決策法則設計對系統績效均具顯著影響。Robot 數量提升雖能降低整體完工時間（TST），但同時增加系統內排隊等待，導致訂單端停留時間（TTIS）上升，呈現產出效率與服務品質之間的績效權衡現象。法則設計方面，具備前置規劃的 Type-I 流程與以最短旅行距離為核心的SDPR法則，在多數情境下皆展現穩健優勢，惟在資源極度充裕且搭配頂尖法則時，Type-II 流程亦能展現潛在極限效能，突顯流程、資源與策略三者之整體匹配性。本研究成果除驗證各作業流程與決策法則對系統效益的穩定貢獻，亦為倉儲業者在流程設計與資源配置決策上提供具體參考依據，並為未來智慧倉儲人機協作優化研究奠定實證基礎。
;As e-commerce grows and warehouse automation advances, distribution centers face increasingly complex picking challenges. With the introduction of collaborative mobile robots (such as LocusBots), designing efficient human-robot collaborative processes has become a core optimization issue. This study investigates a zone-based warehouse system, focusing on the interactions among process design (Type-I and Type-II), resource allocation (number of robots, RN), and picker decision rules (Block Selection Rule, PB, and Robot Selection Rule, PR). In Type-I, pickers first select a Block before choosing a Robot within it; in Type-II, pickers directly select from available Robots in the Zone, bypassing Block selection.
Large-scale simulations and multi-factor ANOVA were conducted, evaluating system performance based on Total System Time (TST) and Total Time In System (TTIS). Results show that increasing robot numbers reduces TST but increases TTIS due to queuing effects, revealing a trade-off between throughput and order responsiveness. Type-I processes, combined with distance-based Shortest Travel Distance Selection (SDPR), consistently achieve robust performance across scenarios. However, under abundant resources and optimal decision rules, Type-II can outperform Type-I, highlighting the importance of aligning process, resource, and decision strategies.
The study offers insights for warehouse managers in designing picking processes and resource planning, while contributing to future research on intelligent human-robot collaboration in warehouse systems. |
