We analyze call center data on properties such as agent heterogeneity, customer patience and breaks. Then we compare simulation models that are different in the ways these properties are modeled. We classify them according to the extend in which they approach the actual service level and average waiting times. We obtain a theoretical understanding on how to distinguish between the model error and other aspects such as random noise. We conclude that modeling explicitly breaks and agent heterogeneity is crucial for obtaining a precise model.

翻译：我们分析了呼叫中心数据中坐席异质性、客户耐心和休息时间等属性的特征。随后，我们对模拟模型进行了比较，这些模型在建模上述属性的方式上存在差异。我们根据模型与实际服务水平及平均等待时间的吻合程度对其进行分类。从理论上，我们明确了如何区分模型误差与其他因素（如随机噪声）。我们得出结论：显式建模休息时间和坐席异质性对于获得精确模型至关重要。