A Flexible Combinatorial Auction Algorithm (FCAA) for Multi-Task Collaborative Scheduling of Heterogeneous UAVs

Highlights: What are the main findings? A Flexible Combinatorial Auction Algorithm (FCAA) is proposed, which is designed with a candidate solution generation mechanism and a candidate solution addition mechanism to reduce the number of candidate solutions prior to combinatorial auctions. By calculating the benefits of candidate solutions based on real-time resource prices, the algorithm can dynamically adjust its priorities, thereby breaking the limitation that existing auction algorithms fail to efficiently and flexibly combine heterogeneous UAV resources for multi-task completion. Simulations show that the FCAA achieves a scheduling success rate of over 88% (with a maximum solution benefit proportion of 83.9%) in small-scale multi-tasking scenarios and a scheduling success rate of 98% (with a maximum solution benefit proportion of 93%) in multi-tasking scenarios, with significantly better time efficiency and solution quality than traditional algorithms. What are the implications of the main findings? It provides an efficient solution to heterogeneous UAV resource scheduling in scenarios such as emergency rescue and intelligent logistics, addressing the low efficiency of traditional algorithms in large-scale tasks and improving the stability of resource allocation in complex environments. Its candidate solution mechanism supports adjusting solution valuations based on practical experience, enabling it to adapt to human–machine collaborative scenarios. To address the inefficiency of collaborative scheduling of heterogeneous Unmanned Aerial Vehicles under resource constraints, particularly in large-scale multi-tasking scenarios, an improved Flexible Combinatorial Auction Algorithm is proposed, leveraging the bidding mechanism of simultaneous ascending auctions. This algorithm is designed with a candidate solution generation mechanism and an addition mechanism, which collectively reduce the number of candidate solutions generated prior to combinatorial auctions. It allows tasks to flexibly combine resources and submit bids. By calculating each candidate solution’s benefit based on real-time resource prices, it dynamically adjusts their priorities to search for the overall optimal multi-task scheduling scheme. It effectively addresses the inability of traditional auction algorithms to dynamically form resource clusters via flexible resource combination to collaboratively complete tasks. Meanwhile, it overcomes the technical bottleneck that existing heuristic algorithms struggle to handle highly complex heterogeneous resource scheduling cases. Simulation experiments show that in small-scale multi-tasking scenarios, the FCAA achieves a scheduling success rate of over 88%, with the maximum solution benefit proportion reaching 83.9%; in multi-tasking scenarios, it achieves a scheduling success rate of 98%, with the maximum solution benefit proportion reaching 93%. Its time efficiency and solution quality are significantly superior to those of traditional algorithms, providing an efficient and stable solution for heterogeneous resource scheduling problems in complex operational environments.