A fuzzy auction genetic algorithm for the vehicle-UAV collaborative delivery problem

Chunping Jiang; Dingjie Wu; Chang Cai; Chunping Jiang; Dingjie Wu; Chang Cai

doi:10.3934/jimo.2026197

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 11: 5831-5854. doi: 10.3934/jimo.2026197

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Research article

A fuzzy auction genetic algorithm for the vehicle-UAV collaborative delivery problem

School of Air Traffic Control, Civil Aviation Flight University of China, Chengdu, 618307, CHINA

Received: 14 September 2026 Revised: 05 October 2026 Accepted: 15 October 2026 Published: 21 October 2026
90B06, 90C59, 68W50, 68T37, 91B26

Efficient task allocation in vehicle-UAV collaborative delivery is challenged by heterogeneous resource capabilities and traffic uncertainty. Auction-based schemes often conflate feasibility screening with execution-level selection and rarely connect fuzzy bidding to subsequent route refinement. To address this issue, we proposed an Improved Fuzzy Auction-Genetic Algorithm (IFAGA) for congestion-aware task allocation and routing optimization in urban logistics. IFAGA combined a two-round fuzzy auction with collaborative evolutionary optimization. The first round screened depots using location proximity, capability matching, and time-window feasibility. The second round selected feasible vehicles or UAVs within the winning depot. The auction output then initialized a genetic stage that repaired assignments and improved workload balance and route efficiency under traffic disturbance. On a fixed 15-task benchmark with 20 independent runs, comparisons against ALNS, Classical-DP, NSGA-Ⅱ, VNS, and a discrete particle swarm optimization baseline (DPSO) showed a trade-off among makespan, total cost, obstacle avoidance, and runtime. A separate event-based rolling horizon experiment with staged task release evaluated completion, lateness, and re-optimization overhead. Across additional random scalability instances, IFAGA achieved the lowest average makespan among the tested task scales, while cost and obstacle avoidance varied by baseline and scale.
- vehicle-UAV collaboration,
- auction mechanism,
- genetic algorithm,
- multi-objective path optimization
Citation: Chunping Jiang, Dingjie Wu, Chang Cai. A fuzzy auction genetic algorithm for the vehicle-UAV collaborative delivery problem[J]. Journal of Industrial and Management Optimization, 2026, 22(11): 5831-5854. doi: 10.3934/jimo.2026197

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Abstract

Efficient task allocation in vehicle-UAV collaborative delivery is challenged by heterogeneous resource capabilities and traffic uncertainty. Auction-based schemes often conflate feasibility screening with execution-level selection and rarely connect fuzzy bidding to subsequent route refinement. To address this issue, we proposed an Improved Fuzzy Auction-Genetic Algorithm (IFAGA) for congestion-aware task allocation and routing optimization in urban logistics. IFAGA combined a two-round fuzzy auction with collaborative evolutionary optimization. The first round screened depots using location proximity, capability matching, and time-window feasibility. The second round selected feasible vehicles or UAVs within the winning depot. The auction output then initialized a genetic stage that repaired assignments and improved workload balance and route efficiency under traffic disturbance. On a fixed 15-task benchmark with 20 independent runs, comparisons against ALNS, Classical-DP, NSGA-Ⅱ, VNS, and a discrete particle swarm optimization baseline (DPSO) showed a trade-off among makespan, total cost, obstacle avoidance, and runtime. A separate event-based rolling horizon experiment with staged task release evaluated completion, lateness, and re-optimization overhead. Across additional random scalability instances, IFAGA achieved the lowest average makespan among the tested task scales, while cost and obstacle avoidance varied by baseline and scale.

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© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)