在分區揀貨倉庫下類Locusbots系統之揀貨人員的Block選取問題

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Title:	在分區揀貨倉庫下類Locusbots系統之揀貨人員的Block選取問題
Authors:	簡怡欣;Jian, Yi-Xin
Contributors:	工業管理研究所
Keywords:	Locusbots 系統;揀貨作業規劃
Date:	2025-07-21
Issue Date:	2025-10-17 11:06:13 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著電子商務的快速發展，物流中心的揀貨作業面臨日益嚴峻的挑戰，尤其在「少量、多樣化」的訂單趨勢下，如何提升揀貨效率成為重要議題。近年來，企業開始引入自動化設備，如Kiva與LocusBots系統，以降低人力成本並提升物流運作效率。其中，LocusBots 系統屬於協作型機器人，其運作模式介於傳統揀貨與完全自動化之間，透過區域劃分與動態任務指派，提升整體作業效率。然而，在LocusBots環境下，如何有效分配揀貨人員至適當的Block，仍缺乏最佳化的決策模型。本研究針對「揀貨人員的Block選取問題」，提出兩類法則進行分析與比較。第一類法則為基本的Block選取機制，而第二類法則進一步引入Robot數量與揀貨人員數量之比值（R/P ratio），透過評估Block的繁忙程度，優先指派揀貨人員至繁忙區域，以提升作業效率。若揀貨人員當前所在的Block已屬繁忙區域，則優先留在原Block執行作業；若無繁忙 Block，則直接使用第一類法則進行選取。此外，本研究亦探討其他揀貨決策問題，包括訂單選取問題、Robot的Zone選取問題、Robot的Block選取問題，以及揀貨人員的Robot選取問題，並針對不同的選取法則進行比較分析。為驗證所提出之方法，本研究透過模擬實驗評估不同揀貨策略組合的影響，並以多項績效指標衡量其成效，如總揀貨時間、訂單完成率與揀貨人員行走距離等。研究結果可望找出適合LocusBots環境的最佳揀貨策略，進一步提升物流中心的揀貨效率，並為未來智慧倉儲的改善提供參考。;With the rapid development of e-commerce, order picking operations in logistics centers face increasing challenges—particularly under the growing trend of small-batch, high-variety orders. Enhancing picking efficiency has thus become a key issue. In response, many enterprises have adopted automation technologies such as Kiva and LocusBots systems to reduce labor costs and improve operational efficiency. Among them, the LocusBots system, a collaborative robot solution, operates between traditional manual picking and full automation. By dividing work zones and dynamically assigning tasks, it aims to improve overall system performance. However, in such environments, an effective strategy for assigning pickers to appropriate blocks remains underexplored. This study focuses on the “Block Selection Problem” for pickers and proposes two categories of decision rules for analysis and comparison. The first category includes basic selection mechanisms, while the second incorporates the robot-to-picker ratio (R/P ratio) to assess block congestion. This approach prioritizes picker assignments to highly congested zones to improve efficiency. When a picker is already located in a busy block, the strategy encourages them to remain; otherwise, in the absence of congested blocks, the system defaults to the first-category rule. Additionally, this study explores other key decision problems in the picking process, including order selection, robot zone selection, robot block selection, and picker-to-robot assignment, evaluating the effectiveness of different strategies in each case. To evaluate the proposed methods, a series of simulation experiments were conducted to examine the effects of various picking strategy combinations. Key performance indicators—such as total picking time, order fulfillment rate, and picker travel distance—were used to assess overall system performance. The results identify optimal picking strategies tailored to LocusBots environments, offering practical insights for enhancing order picking efficiency in logistics centers and contributing to the development of intelligent warehousing systems.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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