Collaborative Research: Staffing and Routing in Service Systems with Uncertain Arrival Rates: An Integrated Stochastic Programming and Asymptotic Analysis Approach

协作研究：到达率不确定的服务系统中的人员配置和路由：综合随机规划和渐近分析方法

• 批准号: 1130346
• 负责人: Tolga Tezcan
• 金额: $ 12.34万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1130346&HistoricalAwards=false
• 关键词: Collaborative; Research; Staffing; Routing; Service

The research objective of this award is to provide managers with sound, practical tools to efficiently balance cost and quality when making staffing and routing decisions in general service systems. Specifically, we consider service systems having multiple customer and server types, and uncertainties in customer arrival volume and server availability. A key complication is that these systems possess uncertainties at multiple scales: operational uncertainty in the service and arrival times, and higher-order uncertainty in the customer volumes and server availability. We will study this problem using an approach that integrates asymptotic analysis, which effectively addresses operational uncertainties, and stochastic programming, which effectively addresses high-order uncertainties. Specifically, we will explore the use of shadow policies for real-time customer routing decisions and use asymptotic analysis to demonstrate their quality for high-volume systems. The results of this analysis will be integrated into a stochastic integer programming approach that determines staffing levels and server schedules. Extensions of the methodology to find good schedules for medium and small-scale service systems will also be studied.Our research will provide managers of complex service systems with practical policies for real-time assignment of customers to servers and methods for server scheduling that provide solutions that are robust to uncertainties in customer volume and server availability. These methods are directly applicable to call center systems and, by better matching servers to requirements, have the potential to reduce costs while providing higher quality service. If successful, the extensions of our methods to small and medium-scale systems will also help managers of health-care systems plan schedules of physicians, nurses, and other health-care providers in the face of a very uncertain and heterogeneous patient population. The PI's will also collaborate to include selective components of each other's discipline into the courses they teach, thereby increasing the range of tools for dealing with uncertainty that their students learn.

这一奖项的研究目标是为管理人员提供合理、实用的工具，以便在一般事务系统中做出人员配置和路线选择决策时有效地平衡成本和质量。具体地说，我们考虑具有多种客户和服务器类型的服务系统，以及客户到达量和服务器可用性的不确定性。一个关键的复杂性是，这些系统在多个范围内具有不确定性：服务和到达时间的运营不确定性，以及客户数量和服务器可用性的高阶不确定性。我们将使用一种结合渐近分析和随机规划的方法来研究这个问题，渐近分析有效地处理操作不确定性，随机规划有效地处理高阶不确定性。具体地说，我们将探索将影子策略用于实时客户路由决策，并使用渐近分析来证明其在大容量系统中的质量。这一分析的结果将纳入确定人员配置水平和服务器时间表的随机整数规划方法。我们的研究将为复杂服务系统的管理者提供实用的客户实时分配到服务器的策略和服务器调度的方法，这些方法提供了对客户数量和服务器可用性的不确定性具有健壮性的解决方案。这些方法直接适用于呼叫中心系统，通过更好地将服务器与需求匹配，有可能在提供更高质量服务的同时降低成本。如果成功，将我们的方法扩展到中小型系统也将帮助卫生保健系统的管理者在面对非常不确定和多样化的患者群体时计划医生、护士和其他卫生保健提供者的时间表。PI还将合作，在他们教授的课程中包括对方学科的选择性组成部分，从而增加他们的学生学习的处理不确定性的工具范围。