Scalable Analysis for Customer Contact Centers

这笔赠款为帮助分析客户联络中心绩效的工具的开发提供资金。客户联络中心是通过聊天室、电子邮件、电话或其他通信渠道向客户提供服务的组织或实体。这些中心在社会中发挥着越来越重要的作用。 客户联络中心可以是任何规模，并出现在各种地方。 大多数美国公司雇用中型或小型呼叫中心。对于多服务器系统的客户联络中心模型，PI建议开发通用的，可扩展的近似各种性能指标，如遇到延迟的客户的比例。这些近似对于小型和大型系统以及不一定是临界负载的系统（想想紧急情况下的呼叫中心）都是计算效率高的。 PI将特别注意现实的模型功能，如一般的服务时间分布和客户放弃，导致开发高维扩散过程的算法的挑战。 拟议的研究将涉及广泛的技术，如偏微分方程的数值解，扩散过程，极值理论，大偏差和分析技术，其中许多是新的在这一领域，如果成功，这项研究将导致一个单一的通用方法呼叫中心的人员配置，适用于各种设置。该算法将被执行。 建议的软件实现提供了一个实用的工具，让一个研究服务时间的变化和耐心的时间分布对系统性能的影响。PI打算将该软件提供给学术研究人员、行业从业人员和大学生。

This grant provides funding for the development of tools that help analyze the performance of customer contact centers. Customer Contact Centers are organizations or entities providing service to customers via chat-box, e-mail, phone, or other communication channels. Such centers play an increasingly dominant role in society. Customer contact centers can be of any size and appear in a variety of places. Most US companies employ moderate- or small-size call centers. For multiserver systems that model customer contact centers, the PIs propose to develop universal, scalable approximations for various performance measures such as the fraction of customers that encounter a delay. These approximations will be computationally efficient for both small and large systems, and for systems that may not necessarily be critically loaded (think of call centers for emergencies). The PIs will pay special attention to realistic model features like general service time distribution and customer abandonment, leading to the challenge of developing algorithms for high dimensional diffusion processes. The proposed research will involve a wide range of techniques like numerical solution of partial differential equations, diffusion processes, extreme value theory, large deviations and analytic techniques; many of which are new in this area.If successful, this research will result in single universal method for call center staffing that is applicable in a variety of settings. The algorithm will be implemented. The proposed software implementation provides a practical tool to allow one to study the impact of service time variability and patience time distribution on system performance. The PIs intend to make the software available to academic researchers, practitioners from industry, and university students.