This paper introduces a control-oriented scheduling approach based on the time slack ratio for adaptive human-to-human queuing systems. The proposed method prioritizes task assignments dynamically, using a slack-based decision-making criterion that ensures efficient task execution under uncertainty. A theoretical analysis establishes competitive performance guarantees by comparing the online and offline scheduling solutions. The results indicate that the algorithm effectively minimizes lateness and task delays in high-load environments where the number of concurrent tasks significantly exceeds the slack ratio parameter. Future research directions include the development of improved heuristics and adaptive scheduling strategies to enhance system performance. © © 2025 The Authors.