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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.

中文翻译：

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排队系统中的结构反馈和行为决策：混合仿真框架


传统的排队模型大多将人工判断和决策排除在系统范围之外，忽略了它们作为系统性能决定因素的作用。然而，经验证据表明，人类行为可以极大地改变系统的输出。在本文中，我们开发了一种混合方法，可以提高我们对个体异构人类代理与聚合系统行为之间相互作用的理解。我们将人类行为响应制定为反馈控制过程，明确捕获智能体的目标和有关系统状态的可用信息，并考虑延迟和可能的扭曲。我们的建模方法利用了重视真实性和代表性的行为建模传统，使公式灵活且易于适应特定情况。我们通过考虑一个带有延迟公告的排队系统来说明我们的方法，该系统常见于服务和制造环境中。我们发现，系统在低利用率和高利用率之间不断循环，从而产生了一种次优模式，具有可预测的高拥塞和低拥塞期，并且总体服务的客户较少。通过将行为反应的效果构建为反馈循环，我们正式分析观察到的系统行为并将其映射到行为决策。所提出的建：头治隹蚣芸梢灾傅枷低成杓，并在关键动态由反馈结构和随机性驱动的场景中提高性能。它为人类行为对排队系统的影响提供了可推广的结构解释。