Abstract This study presents a task-resource allocation model based on effectiveness and solving algorithm. The proposed model allows analysts to obtain tradeoffs between mission completion time and mission effectiveness. The solving algorithm is based on a multi-dimensional dynamic list scheduling algorithm combined with an improved multi-priority list dynamic scheduling algorithm and timed influence nets. Task-platform allocation becomes more flexible with the introduction of a subjective preference. The task-resource allocation method not only calculates the mission completion time, but also predicts the probability of mission success. The feasibility and validation of the proposed method are tested by conducting an adaptive architectures for command and control experiment.