Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning
Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground veh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/7/464 |
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| author | Lin-Yuan Bai Xin-Ya Chen Hai-Feng Ling Yu-Jun Zheng |
| author_facet | Lin-Yuan Bai Xin-Ya Chen Hai-Feng Ling Yu-Jun Zheng |
| author_sort | Lin-Yuan Bai |
| collection | DOAJ |
| description | Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide mobile water supply. To this end, this paper presents an optimization problem of scheduling multiple drones and water supply trucks for wildfire fighting, which allocates burning subareas to drones, routes drones to perform fire-extinguishing operations in burning subareas and reload water between every two consecutive operations, and routes trucks to provide timely water supply for drones. To solve the problem within the limited emergency response time, we propose a deep reinforcement learning method, which consists of an encoder for embedding the input instance features and a decoder for generating a solution by iteratively predicting the subarea selection decision through attention. Computational results on test instances constructed upon real-world wilderness areas demonstrate the performance advantages of the proposed method over a collection of heuristic and metaheuristic optimization methods. |
| format | Article |
| id | doaj-art-9172e722aeb4476e9c5bc1af67d8cfce |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-9172e722aeb4476e9c5bc1af67d8cfce2025-08-20T03:58:27ZengMDPI AGDrones2504-446X2025-06-019746410.3390/drones9070464Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement LearningLin-Yuan Bai0Xin-Ya Chen1Hai-Feng Ling2Yu-Jun Zheng3College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, ChinaCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaCollege of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaWildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide mobile water supply. To this end, this paper presents an optimization problem of scheduling multiple drones and water supply trucks for wildfire fighting, which allocates burning subareas to drones, routes drones to perform fire-extinguishing operations in burning subareas and reload water between every two consecutive operations, and routes trucks to provide timely water supply for drones. To solve the problem within the limited emergency response time, we propose a deep reinforcement learning method, which consists of an encoder for embedding the input instance features and a decoder for generating a solution by iteratively predicting the subarea selection decision through attention. Computational results on test instances constructed upon real-world wilderness areas demonstrate the performance advantages of the proposed method over a collection of heuristic and metaheuristic optimization methods.https://www.mdpi.com/2504-446X/9/7/464firefightingdronesdrone–truck cooperationcooperative schedulingoptimizationdeep reinforcement learning (DRL) |
| spellingShingle | Lin-Yuan Bai Xin-Ya Chen Hai-Feng Ling Yu-Jun Zheng Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning Drones firefighting drones drone–truck cooperation cooperative scheduling optimization deep reinforcement learning (DRL) |
| title | Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning |
| title_full | Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning |
| title_fullStr | Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning |
| title_full_unstemmed | Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning |
| title_short | Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning |
| title_sort | cooperative drone and water supply truck scheduling for wildfire fighting using deep reinforcement learning |
| topic | firefighting drones drone–truck cooperation cooperative scheduling optimization deep reinforcement learning (DRL) |
| url | https://www.mdpi.com/2504-446X/9/7/464 |
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