Conflict-free scheduling of large-scale multi-load AGVs in material transportation network. (February 2022) - Hu, Yue; Yang, Hongbing; Huang, Yi
Highlights: A task allocation method for multi-load AGVs based on the principles of adjacency combination and shortest path. An AGV transportation strategy combining single and two-way paths for the throughput and controllability of both. Propose a method to prevent collisions and deadlocks among AGVs based on timetables of reservations. A novel variable neighborhood search algorithm for the conflict-free scheduling of large-scale multi-load AGVs. Abstract: The process of scheduling an automated guided vehicle (AGV) includes task scheduling, path planning, and traffic control management. The conflict-free scheduling of large-scale multi-load AGVs is a challenging problem in manufacturing logistics and transportation. To solve the problem of scheduling such AGVs in a network logistics system, this study proposes a method of task allocation based on adjacency combination and the shortest path principle. Three priority rules for the mobility of AGVs between nodes are designed, and a transportation strategy that combines single and two-way paths is proposed to reduce computational complexity. By combining with Dijkstra's method, the authors develop a method to prevent deadlocks and collisions between multiple AGVs based on a timetable of reservations that hierarchically handles conflicts among nodes in multiple stages. Such constraints as AGV congestion or deadlock weaken the effectiveness of the shortest distance rule-based solution. Based on the above, a heuristic search method based on variable neighborhood search is further proposed to optimize the problem of multi-AGV task assignment, and a corresponding theorem is given to avoid the generation of unfeasible solutions by the neighborhood operators and improve the efficiency of the solution. The results of experiments show that the proposed method can adequately solve the problem of scheduling multiple AGVs in a large and dense network.