Abstract
With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data.
Highlights
With the development of Industry 4.0 and the rise of e-commerce, the level of intelligence and modernization in logistics has been significantly improved
In the fixed shelves automatic guided vehicles (AGVs) picking system, the operation mode is divided into a single instruction mode and double instruction mode
The cross-aisle is a one-way driving path, and the AGV driving on the same path is in the same direction with the constant speed, so the cross-aisle conflict is not considered
Summary
With the development of Industry 4.0 and the rise of e-commerce, the level of intelligence and modernization in logistics has been significantly improved. Zhang et al [8] modeled an AGV allocation problem in the order-picking task as a resource-constrained scheduling problem, and designed a genetic algorithm to solve it. The research showed that the order task allocation strategy had the greatest impact on the order picking throughput of the system. The model was used for numerical analysis and experiments to optimize the number of the AGVs, the number of the picking stations, and the speed of the AGVs. Lienert et al [12] established a simulation model for the RMFS to predict system operation performance. We construct a semi open-loop queuing network model to evaluate the parameters of the system considering congestion factors such as the throughput and the congestion delay time, and verify the effectiveness and accuracy of the model through simulation and experimental analysis
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