Collaborative optimization of task scheduling and multi-agent path planning in automated warehouses

• Published in: Complex & intelligent systems Vol. 9; no. 5; pp. 5937 - 5948
• Main Authors: Honglin, Zhang, Yaohua, Wu, Jinchang, Hu, Yanyan, Wang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2023; Springer Nature B.V; Springer
• Subjects: Algorithms; Automation; Cloud computing; Collaboration; Combinatorial analysis; Complexity; Computational Intelligence; Data Structures and Information Theory; Design; Efficiency; Engineering; HEFT; Heuristic; Heuristic task scheduling; Intelligent systems; Intelligent warehouse; Internet of Things; Inventory; Literature reviews; Multi-agent path finding; Multiagent systems; Optimization; Optimization algorithms; Order picking; Original Article; Path planning; Picking; Planning; Robots; Scheduling; Task scheduling; Warehouses; Task scheduling; Multi-agent path finding; HEFT; Picking; Intelligent warehouse
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-023-01023-5

Task scheduling (TS) and multi-agent-path-finding (MAPF) are two cruxes of pickup-and-delivery in automated warehouses. In this paper, the two cruxes are optimized simultaneously. Firstly, the system model, task model, and path model are established, respectively. Then, a task scheduling algorithm based on enhanced HEFT, a heuristic MAPF algorithm and a TS- MAPF algorithm are proposed to solve this combinatorial optimization problem. In EHEFT, a novel rank priority rule is used to determine task sequencing and task allocation. In MAPF algorithm, a CBS algorithm with priority rules is designed for path search. Subsequently, the TS-MAPF algorithm which combines EHEFT and MAPF is proposed. Finally, the proposed algorithms are tested separately against relevant typical algorithms at different scales. The experimental results indicate that the proposed algorithms exhibited good performance.