Distributionally Robust Chance-Constrained Task Assignment for Heterogeneous UAVs with Time Windows Under Uncertain Fuel Consumption

This paper addresses the cooperative task assignment problem for heterogeneous unmanned aerial vehicles with time windows considering uncertain fuel consumption. In the scenario where probabilistic fuel consumption exists and its distribution needs to be estimated from historical data samples, we fi...

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Vydáno v:Drones (Basel) Ročník 9; číslo 9; s. 633
Hlavní autoři: Gao, Zhichao, Zheng, Mingfa, Mei, Yu, Zheng, Aoyu, Zhong, Haitao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2025
Témata:
Algorithms
Analysis
Approximation
Assignment problem
Combinatorial analysis
Combinatorial optimization
Constraints
cooperative task assignment
Costs
distributionally robust chance-constrained programming
Drone aircraft
Energy consumption
Fuel consumption
Integer programming
Linear programming
Mixed integer
Monte Carlo simulation
Optimization
Probability distribution
Profit maximization
Random variables
Robustness
Statistical analysis
Supply chain management
time windows
Travel
uncertain fuel consumption
Unmanned aerial vehicles
Windows (intervals)
China
ISSN:2504-446X, 2504-446X
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Shrnutí:This paper addresses the cooperative task assignment problem for heterogeneous unmanned aerial vehicles with time windows considering uncertain fuel consumption. In the scenario where probabilistic fuel consumption exists and its distribution needs to be estimated from historical data samples, we first formulate the problem as a chance-constrained combinatorial optimization problem and utilize the sample average approximation method to solve it. Further, to address the issue of ambiguous distribution, we introduce distributionally robust chance constraints, which consider a set of probability distributions that are contained within a 1-Wasserstein ball centered around the empirical distribution of field data. We approximate the distributionally robust chance-constrained cooperative task assignment problem by applying a CVaR-based tractable approximation such that the problem can be transformed into a deterministic mixed-integer linear programming problem, which can be efficiently solved by state-of-the-art optimization solvers. Finally, we conduct a series of numerical experiments, which not only verify the computational efficiency of the distributionally robust chance-constrainted models but also reduce the degree of constraint violation in out-of-sample tests compared with a sample average approximation method.
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ISSN:2504-446X
2504-446X
DOI:10.3390/drones9090633