Two on-line task re-allocation methodologies capable of re-allocating agents to tasks on-line for minimum task completion time in dynamic environments are presented herein. The first methodology, the Dynamic Nearest Neighbour (DNN) Policy, is proposed for the operation of a fleet of vehicles in a city-like application of the dial-a-ride problem. The second methodology, the Dynamic Re-Pairing Methodology (DRPM) is proposed for the interception of a group of mobile targets by a dynamic team of robotic pursuers, where the targets are assumed to be highly maneuverable with a priori unknown, but real-time trackable, motion trajectories. Extensive simulations and experiments have verified the DNN policy to be tangibly superior to the first-come-first-served and nearest neighbour policies in minimizing customer mean system time, and the DRPM to be tangibly efficient in the optimal dynamic re-pairing of multiple mobile pursuers to multiple mobile targets for minimum total interception time.

Two on-line task re-allocation methodologies capable of re-allocating agents to tasks on-line for minimum task completion time in dynamic environments are presented herein. The first methodology, the Dynamic Nearest Neighbour (DNN) Policy, is proposed for the operation of a fleet of vehicles in a city-like application of the dial-a-ride problem. The second methodology, the Dynamic Re-Pairing Methodology (DRPM) is proposed for the interception of a group of mobile targets by a dynamic team of robotic pursuers, where the targets are assumed to be highly maneuverable with a priori unknown, but real-time trackable, motion trajectories. Extensive simulations and experiments have verified the DNN policy to be tangibly superior to the first-come-first-served and nearest neighbour policies in minimizing customer mean system time, and the DRPM to be tangibly efficient in the optimal dynamic re-pairing of multiple mobile pursuers to multiple mobile targets for minimum total interception time.