{"title":"Ant Colony Systems for Optimizing Sequences of Airspace Partitions","authors":"D. Gianazza, N. Durand","doi":"10.1109/AIDA-AT48540.2020.9049206","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce an Ant Colony System algorithm which finds optimal or near-optimal sequences of airspace partitions, taking into account some constraints on the transitions between two successive airspace configurations. The transitions should be simple enough to allow air traffic controllers to maintain their situation awareness during the airspace configuration changes. For the same reason, once a sector is opened it should remain so for a minimum duration. The Ant Colony System (ACS) finds a sequence of airspace configurations minimizing a cost related to the workload and the usage of manpower resources, while satisfying the transition constraints. This approach shows good results in a limited time when compared with a previously proposed $A$ * algorithm on some instances from the french air traffic control center of Aix (East qualification zone) where the $A$ * algorithm exhibited high computation times.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIDA-AT48540.2020.9049206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we introduce an Ant Colony System algorithm which finds optimal or near-optimal sequences of airspace partitions, taking into account some constraints on the transitions between two successive airspace configurations. The transitions should be simple enough to allow air traffic controllers to maintain their situation awareness during the airspace configuration changes. For the same reason, once a sector is opened it should remain so for a minimum duration. The Ant Colony System (ACS) finds a sequence of airspace configurations minimizing a cost related to the workload and the usage of manpower resources, while satisfying the transition constraints. This approach shows good results in a limited time when compared with a previously proposed $A$ * algorithm on some instances from the french air traffic control center of Aix (East qualification zone) where the $A$ * algorithm exhibited high computation times.
在本文中,我们引入了一种蚁群系统算法,该算法考虑了两个连续空域结构之间转换的一些约束条件,找到了空域划分的最优或近最优序列。转换应该足够简单,以允许空中交通管制员在空域配置变化期间保持其态势感知。出于同样的原因,一个扇区一旦开放,就应该在最短时间内保持开放。蚁群系统(ACS)在满足过渡约束的情况下,找到与工作量和人力资源使用相关的成本最小的空域配置序列。与先前提出的$ a $ *算法相比,该方法在有限的时间内取得了良好的效果,该算法在法国空中交通管制中心Aix(东部合格区)的一些实例中显示出较高的计算时间。