基于时空位置信息的自动化集装箱码头多 AGV 动态滚动调度模型

IF 4.8 2区 环境科学与生态学 Q1 OCEANOGRAPHY
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引用次数: 0

摘要

海运是一种重要的运输方式,而自动化集装箱港口在提高海运效率方面发挥着重要作用,代表着海运物流的主要趋势。为确保集装箱自动化港口的高效稳定运行,在研究和应用方法方面存在挑战,例如自动导引车(AGV)的合理任务分配和快速无冲突路径规划。本文通过设计一种新的任务分配方法和提出一种改进的 AGV 自动避障 A-star 算法(IAOA-A 算法)来研究 AGV 的组合优化方案,以解决动态避障情况下的这些问题。首先,为了提高任务分配的有效性和及时性,本文重新定义了集装箱紧急程度(CUL)的计算指标,用于路径优化算法的权重计算。其次,为了实现 AGV 的无碰撞路径规划,本文设计了一个二维时空障碍物模型来描述 AGV 的动态位置信息,并通过优化搜索空间和改进评估函数策略来加速搜索过程。最后,将这些方法集成到滚动调度模型中,实现了 AGV 的全局无冲突路径规划。AGV 运输任务的实验比较表明,基于 CUL 的分配规则能有效降低平均相对百分比差(ARPD),从而更及时地完成任务。与传统的 A-star 算法相比,所提出的 IAOA-A 算法提高了 24.45% 的时间效率,可视化结果表明搜索节点数量显著减少。在不同 AGV 数量和任务规模的测试中,结果表明本文提出的滚动调度模型可以高效、快速地执行 AGV 的全局调度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic rolling scheduling model for multi-AGVs in automated container terminals based on spatio-temporal position information

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.

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来源期刊
Ocean & Coastal Management
Ocean & Coastal Management 环境科学-海洋学
CiteScore
8.50
自引率
15.20%
发文量
321
审稿时长
60 days
期刊介绍: Ocean & Coastal Management is the leading international journal dedicated to the study of all aspects of ocean and coastal management from the global to local levels. We publish rigorously peer-reviewed manuscripts from all disciplines, and inter-/trans-disciplinary and co-designed research, but all submissions must make clear the relevance to management and/or governance issues relevant to the sustainable development and conservation of oceans and coasts. Comparative studies (from sub-national to trans-national cases, and other management / policy arenas) are encouraged, as are studies that critically assess current management practices and governance approaches. Submissions involving robust analysis, development of theory, and improvement of management practice are especially welcome.
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