A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA
The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optim...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/7/501 |
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| author | Yongchao Zhang Wei Xu Helin Ye Zhuoyong Shi |
| author_facet | Yongchao Zhang Wei Xu Helin Ye Zhuoyong Shi |
| author_sort | Yongchao Zhang |
| collection | DOAJ |
| description | The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems. |
| format | Article |
| id | doaj-art-e39b994de97243b49db2e17bef41ab4a |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e39b994de97243b49db2e17bef41ab4a2025-08-20T02:45:55ZengMDPI AGDrones2504-446X2025-07-019750110.3390/drones9070501A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBAYongchao Zhang0Wei Xu1Helin Ye2Zhuoyong Shi3School of Electrical and Information Engineering, Xi’an Jiaotong University City College, Xi’an 710018, ChinaSchool of Electrical and Information Engineering, Xi’an Jiaotong University City College, Xi’an 710018, ChinaSchool of Electrical and Information Engineering, Xi’an Jiaotong University City College, Xi’an 710018, ChinaFaculty of Science, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077, SingaporeThe joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems.https://www.mdpi.com/2504-446X/9/7/501unmanned aerial vehiclesconsensus bundle algorithmintegrated UAV schedulingtask allocation |
| spellingShingle | Yongchao Zhang Wei Xu Helin Ye Zhuoyong Shi A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA Drones unmanned aerial vehicles consensus bundle algorithm integrated UAV scheduling task allocation |
| title | A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA |
| title_full | A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA |
| title_fullStr | A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA |
| title_full_unstemmed | A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA |
| title_short | A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA |
| title_sort | two stage optimization framework for uav fleet sizing and task allocation in emergency logistics using the gwo and cbba |
| topic | unmanned aerial vehicles consensus bundle algorithm integrated UAV scheduling task allocation |
| url | https://www.mdpi.com/2504-446X/9/7/501 |
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