| 摘要: | 近年來，資訊科技的迅速發展和行動網絡的普及使得電子商務成為市場主流，這也帶來了「少量、多樣化」的市場需求趨勢。這樣的轉變不僅增加了物流中心的作業難度，尤其在揀貨作業方面更為明顯。根據De Koster et al.（2007）的研究顯示，物流中心目前仍然以勞力為主，揀貨作業成本相當高昂，佔總成本的50%以上。為因應這樣的市場環境，引入自動化設備並合理規劃揀貨策略成為提升物流中心成本、產能和效率的重要關鍵。
類Locusbots系統的優勢在於其可靈活增減揀貨機器人的特性，有效應對訂單淡旺季的挑戰。該系統採用動態路徑規劃，即時更新揀貨環境狀態，巧妙避開各種障礙物，並計劃最優的揀貨路徑。此外，在類Locusbots系統中，揀貨員不需在倉庫中四處移動，只需待在指定揀貨區域，由揀貨機器人運送訂單和揀貨箱，使揀貨員能夠專注進行揀貨作業。這不僅能降低人力成本，同時提高揀貨作業的效率和準確性。
基於上述理由，本研究聚焦於類Locusbots系統中的揀貨策略，將一物流揀貨倉庫區分為多個揀貨區塊（Block）數相同的揀貨區域（Zone），對「Robot的揀貨區域（Zone）選擇問題」與「Robot的揀貨區塊（Block）選擇問題」分別數個法則，並提出兩種同時考慮了上述兩個問題的不同揀貨流程，期望透過本研究的模擬與分析，找出最佳的揀貨策略組合以及揀貨流程。
;In recent years, the rapid development of information technology and the proliferation of mobile networks have made e-commerce the mainstream market. This has also brought about a trend of "small quantities, diverse varieties" in market demand. Such a shift not only increases the operational difficulty of logistics centers, but it is particularly evident in the picking operations. According to the research by De Koster et al. (2007), logistics centers are still predominantly labor-intensive, and the cost of picking operations is quite high, accounting for more than 50% of the total cost. To cope with this market environment, introducing automated equipment and reasonably planning picking strategies have become key factors in improving the cost, capacity, and efficiency of logistics centers.
The advantage of systems like LocusBots lies in their ability to flexibly increase or decrease the number of picking robots, effectively meeting the challenges of order fluctuations during peak and off-peak seasons. This system adopts dynamic path planning, which updates the picking environment status in real-time, skillfully avoids various obstacles, and plans the optimal picking path. Moreover, in a LocusBots-like system, pickers do not need to move around the warehouse; they only need to stay in designated picking zones. The picking robots transport orders and picking bins, allowing pickers to focus solely on the picking operations. This not only reduces labor costs but also improves the efficiency and accuracy of picking operations.
Based on the above reasons, this study focuses on the picking strategies in systems like LocusBots. It divides a logistics picking warehouse into several picking blocks (Blocks) and an equal number of picking zones (Zones). The study formulates several rules for the "Robot’s Zone Selection Problem" and the "Robot’s Block Selection Problem," and proposes two different picking processes that consider both of these issues simultaneously. Through simulation and analysis, this research aims to identify the optimal combination of picking strategies and processes. |
