Skip to content
DraconiticResearch
Search Sources API
201,818 studies31 sourcesUpdated 21 min ago
Peer-reviewedCC BY 4.0Mathematics

Multi-UAV Emergency Scheduling Under Demand Accumulation and Payload-Dependent Energy

  • Haifei Yang1
  • Ganwei Cai2,3,4
  • Junjie Gong1,5
  • Yu Long4
5 affiliations
  1. 1School of Business, Guilin University of Electronic Technology, Guilin 541004, China
  2. 2School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241000, China
  3. 3School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
  4. 4School of Mechanical Engineering, Guangxi University, Nanning 530004, China
  5. 5Key Laboratory of Intelligence Integrated Automation in Guangxi Universities, Guilin University of Electronic Technology, Guilin 541004, China
Open at sourceDOICite

Abstract

Emergency delivery by multiple unmanned aerial vehicles (UAVs) requires scheduling decisions that account for demand accumulation, payload-dependent energy consumption, and the feasibility of successive sorties. To address this coupled problem, this paper proposes an energy-aware discrete black-winged kite algorithm with local search and insertion screening (EDBKA-LS-I). The model minimizes cumulative unmet demand exposure subject to payload, battery, return energy, and mission time constraints. Payload-dependent energy is calibrated from public flight records, and a sufficient speed dominance condition supports preprocessing under no-wait landing delivery. Geometric and random screening select insertion candidates for complete evaluation, while bounded local search refines UAV assignments, the sortie order, and customer sequences. Nine synthetic and public layout-derived instances were tested with twenty seeds per method under matched evaluation budgets. Three instances were additionally tested with five seeds per method under a six-second search budget. Relative to adaptive large neighborhood search with identical screening, EDBKA-LS-I reduced the mean cumulative unmet demand exposure by 7.74% and 7.93% under the respective budgets. EDBKA-LS-I also obtained lower mean exposure than variable neighborhood search on seven of nine instances under the evaluation budget. EDBKA-LS-I achieved complete service in all main comparisons. The energy model attained a mean absolute prediction error of 0.993 Wh on forty held-out delivery flights.

Related

  1. Forecast-Residual-Based Chance-Constrained Scheduling of Local Energy Communities under PV and Demand Uncertainty
    Franco Cárdenas González, Fernando García-Muñoz
    arXivPreprintCC BY-NC-ND 4.0Full textOptimisation & controlarXiv
  2. Noise-Aware Fleet Scheduling for On-Demand Urban Air Mobility with Alternative Flight Paths and Cruise Separation
    Haoye Chen, Jan Kronqvist, Wilco Burghout +2
    arXivPreprintarXiv licenceFull textOptimisation & controlarXiv
  3. Forecasting and Low-Carbon Demand Response Scheduling of Crane-Group Station-Service Loads for Grid-Friendly Hydropower Station Operation
    Wanwan Wei, Shangfeng Guo, Xiaolong Cheng +2
    EAI Endorsed Transactions on Energy WebPeer-reviewedCC BY-NC-SAFull textGeneral mathematicsDOAJ
  4. Integrating YOLOv8-based disaster detection with deep reinforcement learning for closed-loop UAV emergency response scheduling: A simulation study
    Chunsheng Zhang, Ruichun He
    PLOS OnePeer-reviewedCC BYFull textNeuroscienceStatisticsPLOSEurope PMCPMC
  5. Stochastic Appointment Scheduling with Patient-and-Time-Dependent Probability Distributions
    Soheyl Khalilpourazari, Hossein Hashemi Doulabi
    arXivPreprintCC BY 4.0Full textOptimisation & controlarXiv

Cite

BibTeXRIS
BibTeX
@article{yang2026multiuav,
  title = {{Multi-UAV Emergency Scheduling Under Demand Accumulation and Payload-Dependent Energy}},
  author = {Haifei Yang and Ganwei Cai and Junjie Gong and Yu Long},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14213849},
  url = {https://www.mdpi.com/2227-7390/14/21/3849},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multi-UAV Emergency Scheduling Under Demand Accumulation and Payload-Dependent Energy
AU  - Haifei Yang
AU  - Ganwei Cai
AU  - Junjie Gong
AU  - Yu Long
PY  - 2026
DA  - 2026/10/22
JO  - Mathematics
DO  - 10.3390/math14213849
UR  - https://www.mdpi.com/2227-7390/14/21/3849
AB  - Emergency delivery by multiple unmanned aerial vehicles (UAVs) requires scheduling decisions that account for demand accumulation, payload-dependent energy consumption, and the feasibility of successive sorties. To address this coupled problem, this paper proposes an energy-aware discrete black-winged kite algorithm with local search and insertion screening (EDBKA-LS-I). The model minimizes cumulative unmet demand exposure subject to payload, battery, return energy, and mission time constraints. Payload-dependent energy is calibrated from public flight records, and a sufficient speed dominance condition supports preprocessing under no-wait landing delivery. Geometric and random screening select insertion candidates for complete evaluation, while bounded local search refines UAV assignments, the sortie order, and customer sequences. Nine synthetic and public layout-derived instances were tested with twenty seeds per method under matched evaluation budgets. Three instances were additionally tested with five seeds per method under a six-second search budget. Relative to adaptive large neighborhood search with identical screening, EDBKA-LS-I reduced the mean cumulative unmet demand exposure by 7.74% and 7.93% under the respective budgets. EDBKA-LS-I also obtained lower mean exposure than variable neighborhood search on seven of nine instances under the evaluation budget. EDBKA-LS-I achieved complete service in all main comparisons. The energy model attained a mean absolute prediction error of 0.993 Wh on forty held-out delivery flights.
SN  - 2227-7390
ER  -