| dc.description.abstract | 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. | en_US |