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Robust Optimization for Cooperative Task Assignment of Heterogeneous Unmanned Aerial Vehicles with Time Window Constraints

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Publicado en:Axioms vol. 14, no. 3 (2025), p. 184
Autor principal: Gao, Zhichao
Otros Autores: Zheng, Mingfa, Zhong, Haitao, Yu, Mei
Publicado:
MDPI AG
Materias:
Monte Carlo simulation
Linear programming
Mathematical analysis
Random variables
Integer programming
Combinatorial analysis
Unmanned aerial vehicles
Assignment problem
Transportation models
Optimization techniques
Genetic algorithms
Decision making
Optimization
Mixed integer
Windows (intervals)
Parameter uncertainty
Fuel consumption
Constraints
Energy consumption
Robustness
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:The cooperative task assignment problem with time windows for heterogeneous multiple unmanned aerial vehicles is an attractive complex combinatorial optimization problem. In reality, unmanned aerial vehicles’ fuel consumption exhibits uncertainty due to environmental factors or operational maneuvers, and accurately determining the probability distributions for these uncertainties remains challenging. This paper investigates the heterogeneous multiple unmanned aerial vehicle cooperative task assignment model that incorporates time window constraints under uncertain environments. To model the time window constraints, we employ the big-M method. To address the uncertainty in fuel consumption, we apply an adjustable robust optimization approach combined with duality theory, which allows us to derive the robust equivalent form and transform the model into a deterministic mixed-integer linear programming problem. We conduct a series of numerical experiments to compare the optimization results across different objectives, including maximizing task profit, minimizing total distance, minimizing makespan, and incorporating three different time window constraints. The numerical results demonstrate that the robust optimization-based heterogeneous multiple unmanned aerial vehicle cooperative task assignment model effectively mitigates the impact of parameter uncertainty, while achieving a balanced trade-off between robustness and the optimality of task assignment objectives.
ISSN:2075-1680
DOI:10.3390/axioms14030184
Fuente:Engineering Database