A. P. P. Perwira Redi, Rahmad Inca Liperda, B. M. Sopha, Anna Maria Sri Asih, Nandini N. Sekaringtyas, Handina B. Astiana
{"title":"基于无人机的两梯队车辆路径问题的地形测绘评估","authors":"A. P. P. Perwira Redi, Rahmad Inca Liperda, B. M. Sopha, Anna Maria Sri Asih, Nandini N. Sekaringtyas, Handina B. Astiana","doi":"10.1109/ICST50505.2020.9732812","DOIUrl":null,"url":null,"abstract":"This study considers the two-echelon vehicle routing problem using drones in the postdisaster situation. The problem takes into account using a collaboration between a drone with a ground vehicle to conduct an information gathering so-called mapping operation. Each ground vehicle is associated with a drone. The first echelon depicts the routes travelled from depot to stopover point and from a stopover point to another stopover point. The first echelon is travelled by a ground vehicle to extend the limitation of drone's range coverage to gather information. The second echelon is the assignment of the drone to the target point. A target point is an area being mapped or in this case that is affected by the disaster. The problem is modelled as an integer linear programming problem denoted as 2EVRP-MOD. It is assumed that drone can only be released from a stopover point. The mapping operation is associated with the amount of area being covered at each target area. The objective is to minimize the total mapping operation time. The entire mapping operation time is limited by the drone flying capacity limit. The model is tested on a real-case dataset in Bekasi, Indonesia. The computational results show that the model can effectively provide a solution for the 2EVRP-MOD.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Relief Mapping Assessment using Two-Echelon Vehicle Routing Problem with Drone\",\"authors\":\"A. P. P. Perwira Redi, Rahmad Inca Liperda, B. M. Sopha, Anna Maria Sri Asih, Nandini N. Sekaringtyas, Handina B. Astiana\",\"doi\":\"10.1109/ICST50505.2020.9732812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study considers the two-echelon vehicle routing problem using drones in the postdisaster situation. The problem takes into account using a collaboration between a drone with a ground vehicle to conduct an information gathering so-called mapping operation. Each ground vehicle is associated with a drone. The first echelon depicts the routes travelled from depot to stopover point and from a stopover point to another stopover point. The first echelon is travelled by a ground vehicle to extend the limitation of drone's range coverage to gather information. The second echelon is the assignment of the drone to the target point. A target point is an area being mapped or in this case that is affected by the disaster. The problem is modelled as an integer linear programming problem denoted as 2EVRP-MOD. It is assumed that drone can only be released from a stopover point. The mapping operation is associated with the amount of area being covered at each target area. The objective is to minimize the total mapping operation time. The entire mapping operation time is limited by the drone flying capacity limit. The model is tested on a real-case dataset in Bekasi, Indonesia. The computational results show that the model can effectively provide a solution for the 2EVRP-MOD.\",\"PeriodicalId\":125807,\"journal\":{\"name\":\"2020 6th International Conference on Science and Technology (ICST)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Science and Technology (ICST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICST50505.2020.9732812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Science and Technology (ICST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICST50505.2020.9732812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Relief Mapping Assessment using Two-Echelon Vehicle Routing Problem with Drone
This study considers the two-echelon vehicle routing problem using drones in the postdisaster situation. The problem takes into account using a collaboration between a drone with a ground vehicle to conduct an information gathering so-called mapping operation. Each ground vehicle is associated with a drone. The first echelon depicts the routes travelled from depot to stopover point and from a stopover point to another stopover point. The first echelon is travelled by a ground vehicle to extend the limitation of drone's range coverage to gather information. The second echelon is the assignment of the drone to the target point. A target point is an area being mapped or in this case that is affected by the disaster. The problem is modelled as an integer linear programming problem denoted as 2EVRP-MOD. It is assumed that drone can only be released from a stopover point. The mapping operation is associated with the amount of area being covered at each target area. The objective is to minimize the total mapping operation time. The entire mapping operation time is limited by the drone flying capacity limit. The model is tested on a real-case dataset in Bekasi, Indonesia. The computational results show that the model can effectively provide a solution for the 2EVRP-MOD.