Chen Xiong , Cheng Wang , Shaorui Zhou , Xiaoming Song
{"title":"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","authors":"Chen Xiong , Cheng Wang , Shaorui Zhou , Xiaoming Song","doi":"10.1016/j.ocecoaman.2024.107349","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54698,"journal":{"name":"Ocean & Coastal Management","volume":"258 ","pages":"Article 107349"},"PeriodicalIF":4.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic rolling scheduling model for multi-AGVs in automated container terminals based on spatio-temporal position information\",\"authors\":\"Chen Xiong , Cheng Wang , Shaorui Zhou , Xiaoming Song\",\"doi\":\"10.1016/j.ocecoaman.2024.107349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":54698,\"journal\":{\"name\":\"Ocean & Coastal Management\",\"volume\":\"258 \",\"pages\":\"Article 107349\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean & Coastal Management\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S096456912400334X\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OCEANOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean & Coastal Management","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096456912400334X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
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.
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.