Abstract: Tasks are often executed in a highly parallel and concurrent mode in spatiotemporal networks. Specified task distribution benefits the decreases of the complexity of cooperation among system components. As one of the important and pivotal issues in collaborating, spatiotemporal constraints must be taken into consideration in task allocation. Otherwise, the assigned tasks will be in conflict frequently during the execution step, which will lead to a sharp decline of the overall system performance. In order to avoid conflicts and optimize the team performance and the interoperability, the following three aspects are mainly investigated: 1) taking airport parking space scheduling as an example, modeling the assignment problem under time and space constraints via group role assignment (GRA), which is a sub-model of Role-Based Collaboration (RBC) and its general model E-CARGO; 2) analyzing situation of different agents undertaking different roles, and different agents undertaking the same role, so as to establish the qualification matrix and the collaboration matrix; 3) decoupling and dissolving spatiotemporal constraints, and furthermore, pursuing an integer programming, which is used to solve the multi-objective balance between the passengers' preference and the utilization of airport parking space. Large-scale simulation experiments and results indicate that, this method is general, valid and reliable. In addition, compared with the traditional GRA model, the subject performance is increased by 6.21%, the object preference is increased by 9.72%, and the second level running speed can meet the requirements of rapid assignment response in complex spatiotemporal networks.

Key words: spatiotemporal network, role-based collaboration (RBC), E-CARGO, group role assignment (GRA), conflict resolution