Dynamic rolling scheduling model for multi-AGVs in automated container terminals based on spatio-temporal position information

Abstract

Maritime transport is a crucial mode of transportation, with automated container ports playing a significant role in enhancing maritime efficiency and representing a key trend in maritime logistics. Challenges exist in researching and applying methods to ensure efficient and stable operation of automated container ports, such as rational task assignment for Automated Guided Vehicles (AGVs) and rapid conflict-free path planning. This paper studies a combinatorial optimization scheme for AGVs by designing a new task assignment method and proposing an improved AGV automatic obstacle avoidance A-star algorithm (IAOA-A algorithm) to address these problems in dynamic obstacle avoidance situations. Firstly, to enhance the effectiveness and timeliness of task allocation, this paper redefines the calculation index of the container urgency level (CUL) for use in the path optimization algorithm’s weight calculation. Secondly, to achieve collision-free path planning for AGVs, a two-dimensional spatio-temporal obstacle model is designed to describe the dynamic location information of AGVs, and the search process is accelerated by optimizing the search space and improving the evaluation function strategy. Finally, these methods are integrated into a rolling scheduling model to achieve global conflict-free path planning for AGVs. Experimental comparisons on AGV transport tasks show that the allocation rule based on CUL effectively reduces the average relative percentage difference (ARPD), resulting in more timely task completion. Compared to the traditional A-star algorithm, the proposed IAOA-A algorithm improves time efficiency by 24.45%, with visualization results indicating a significant reduction in the number of search nodes. In tests with varying numbers of AGVs and task scales, the results demonstrate that the rolling scheduling model proposed in this paper can efficiently and quickly perform global scheduling of AGVs.