Cooperative Task Allocation for Heterogeneous Unmanned Delivery Vehicles

Abstract

The task allocation problem of unmanned delivery vehicles (UDVs) is critical and has attracted much attention in the field of intelligent delivery. However, most current studies are limited to applications since they simply regard all UDVs to be dispatched as identical. In this paper, a cooperative task allocation method is proposed for dispatching heterogeneous UDVs to complete delivery tasks. First, the cooperative task allocation problem is formulated as a multi-objective optimization problem subject to constraints, where two conflicting objectives are total distance and differential workload balance. The differential workload balance objective is to minimize the highest difference between the vehicle ability and the workload of an individual vehicle. Second, a discrete particle swarm optimization algorithm with a priority-guided correction strategy is proposed to solve the multi-objective optimization problem. The priority-guided correction strategy guarantees the particle search in a feasible region and improves the search efficiency by utilizing heterogeneous vehicle information. Third, a multi-mutation operator local search strategy is embedded to avoid particles easily trap** in local optima during the search process. The strategy enhances the local search ability by combining three mutation operators, the interchange mutation operator, the insertion mutation operator, and the exchange mutation operator. Simulation results demonstrate the effectiveness and advantages of the proposed optimization algorithm for solving the cooperative task allocation problem of heterogeneous unmanned delivery vehicle.