Centralized Multi-Objective UAV–UGV Task Allocation for Disaster Relief Material Distribution with Mobile UAV Bases

Coordinating unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for post-disaster emergency material distribution requires joint decisions on task allocation, routing, delivery quantities, and rendezvous scheduling. Mobile UGV bases and split deliveries increase operational flexibility but introduce strong dependencies between aerial and ground operations. This paper investigates a centralized multi-objective multi-robot task allocation problem with static, known demands, in which UGVs transport supplies and support UAV launch, recovery, and battery replacement. A three-objective model is formulated to minimize weighted demand completion time, operating cost, and maximum normalized workload, subject to accessibility, payload, inventory, energy, and synchronization constraints. To solve the resulting constrained combinatorial optimization problem, we propose HV-ADEA, a hypervolume-guided adaptive evolutionary algorithm. A hybrid representation jointly encodes UGV stop sequences, UAV–interval task assignments, and split delivery quantities, while a synchronized decoder derives execution times and evaluates feasibility. A bounded iterative repair mechanism addresses coupled constraint violations. Domain-specific variation operators perform joint mobile base–sortie adjustment, aerial–ground quantity transfer, delivery splitting and merging, and synchronization bottleneck reduction to explore alternative allocation and routing plans. An experimental protocol is established using six test configurations of increasing scale and five baseline algorithms: NSGA-II, CCMO, BiCo, EMCMO, and C3M. The test results show that the algorithm we propose is competitive in most of the test cases.