A Lifelong Multi-Shuttle Scheduling Framework for the AS/RS System

Efficient scheduling of multi-shuttle is crucial for optimizing the performance of Automated Storage/Retrieval Systems (AS/RS). Multi-Agent Path Finding (MAPF) techniques play a pivotal role in addressing scheduling challenges by guaranteeing collision-free paths for multiple agents, however the assumptions of MAPF cannot meet the practical applications, such as 1) agents move at constant speeds and change directions instantaneously, 2) agents remain stationary at their destinations, 3) agents have different destinations. In this paper, we propose a novel lifelong multi-shuttle scheduling framework (LMSSF) to fill this gap based on the AS/RS system implemented in Hunan, China. LMSSF can perform re-planning and execution occur simultaneously, ensuring robust performance even in dynamic and uncertain environment. To apply MAPF to practical scenarios, we propose task conflict resolution, an improved single-shot MAPF and a path constraint mechanism to ensure collision-free movement of shuttles and improve the throughput performance of AS/RS system. Empirical evaluations on throughput and occupancy rate of outbound stations demonstrate the superiority of our proposed algorithm. Finally, LMSSF is applied to a real-world system and the experimental results show a 31.8% improvement in throughput compared to conventional strategies.Note to Practitioners—The motivation of this article stems from the challenges posed by the assumptions inherent in most MAPF algorithms when applied to practical AS/RS environments. Traditional MAPF algorithms typically compute discrete collision-free paths based on predefined start and goal locations. However, in real-world scenarios, tasks are continually generated, shuttles are highly dynamic, and various environmental constraints exist. To address these challenges, we propose the LMSSF as a means to adapt existing MAPF algorithms for practical AS/RS systems. This framework incorporates task conflict resolution, path planning optimization, and efficient shuttle control mechanisms. The paths obtained from the MAPF solver can be seamlessly integrated into our framework with minor format conversions. Through comprehensive testing, our algorithm demonstrates significantly improved throughput performance compared to traditional strategies.