Structural feedback and behavioral decision making in queuing systems: A hybrid simulation framework

Traditional queuing models mostly leave human judgment and decision making outside the scope of the system, ignoring their role as determinants of system performance. However, empirical evidence has shown that human behavior can substantially alter the system's output. In this paper, we develop a hybrid approach that improves our understanding of the interplay between individual heterogeneous human agents and aggregate system behavior. We formulate human behavioral responses as feedback control processes, explicitly capturing the agent's objectives and available information about the system's state, accounting for delays and possible distortions. Our modeling approach taps into a behavioral modeling tradition that values realism and representativeness, making the formulations flexible and easily adaptable to specific situations. We illustrate our approach by considering a queuing system with delay announcement, commonly found in service and manufacturing settings. We find that the system continuously cycles between periods of low and high utilization, creating a suboptimal mode with predictable periods of high and low congestion and fewer customers served overall. By structuring the effect of behavioral responses as feedback loops, we formally analyze the observed system behavior and map it to behavioral decisions. The proposed modeling and analysis framework can guide system design and improve performance in scenarios where key dynamics are driven by both feedback structure and stochasticity. It provides generalizable structural explanations of the impact of human behavior in queuing systems.