| Abstract: | 近年來資訊科技迅速發展，再加上行動網絡的普及化，造成電子商務的盛行，因此市場需求逐漸轉變為「少量、多樣化」，市場需求的改變同時也提升了物流中心的作業難度，其中對於揀貨作業更甚明顯。根據De Koster et al.（2007）的研究指出，目前大多數的物流中心仍屬於勞力密集的產業，揀貨作業不僅相當耗費成本，更是一種屬於勞力密集的活動，在物流中心裡與揀貨作業相關的人力佔了50%以上。因此為了因應「少量、多樣化」需求的時代來臨，適時地導入自動化設備，並規劃一個合適的揀貨策略，將對物流中心的成本、產能以及效率有著決定性的影響。
由於類Locusbots系統可以增加或減少Robot的數目，因此可以有效地解決訂單淡、旺季的問題。除此之外Locusbots可以利用動態路徑規劃，更新揀貨環境狀態，有效避開路上各種障礙物，並規劃最有利的揀貨路徑。本研究旨在「類Locusbots系統」的分區揀貨環境下，探討類Locusbots系統之揀貨人員的作業流程（Type - Ⅰ）（先使用揀貨人員Block選取問題再使用揀貨人員Robot選取問題選取Robot進行揀貨作業）與系統之揀貨人員的作業流程（Type - Ⅱ）（僅使用揀貨人員Robot選取問題選取Robot進行揀貨作業）之績效比較與揀貨人員的「Robot選取問題」在類Locusbots系統之揀貨人員的作業流程（Type - Ⅰ） 與類Locusbots系統之揀貨人員的作業流程（Type - Ⅱ）中之績效比較，應用模擬軟體實驗之結果，期望找出最佳的揀貨人員策略組合，以降低揀貨時間並提升揀貨效率，並對未來之類似研究有相對貢獻。
;In recent years, the rapid development of information technology, coupled with the widespread use of mobile networks, has led to the prevalence of e-commerce. Consequently, market demand has gradually shifted to "small quantity, diverse variety," which has simultaneously increased the operational difficulty of logistics centers, particularly in picking operations. According to the research by De Koster et al. (2007), most logistics centers are still labor-intensive industries. Picking operations are not only costly but also labor-intensive, with more than 50% of the workforce in logistics centers being involved in picking activities. Therefore, to respond to the era of "small quantity, diverse variety" demand, the timely introduction of automated equipment and the planning of an appropriate picking strategy will have a decisive impact on the costs, productivity, and efficiency of logistics centers.
Since systems like Locusbots can increase or decrease the number of robots, they can effectively address the issue of fluctuating order volumes during peak and off-peak seasons. Additionally, Locusbots can utilize dynamic path planning to update the state of the picking environment, effectively avoiding various obstacles on the way and planning the most advantageous picking path. This study aims to explore the operational processes of pickers in a "Locusbots-like system" under a zone picking environment. Specifically, it compares the performance of two picker workflows: Type - I (using the Picker Block Selection Problem first, then the Picker Robot Selection Problem to select robots for picking) and Type - II (using only the Picker Robot Selection Problem to select robots for picking). By applying simulation software experiments, the study aims to identify the optimal picker strategy combination to reduce picking time and improve picking efficiency, contributing to similar future research. |
