Optimal Staffing Policies to Stabilize Performance at Target Levels in Service Networks with Time-Varying Arrivals

This research is focused on the development, implementation, and evaluation of new and effective policies to set appropriate staffing levels in large-scale multiserver queuing networks that are equipped with complex features typically found in real-world service systems. First, arrival rates in service systems typically vary significantly over time, which is not accounted for by standard queuing models. Second, abandonment by waiting customers, which corresponds to patients leaving without being seen by a care provider, or to callers hanging up in a call center, can significantly alter system performance. In addition, empirical data show that neither service time nor abandonment time is exponentially distributed. Finally, service systems often exhibit complicated network structures, such as the following: (a) retrials by customers who have received service but still need more service; and (b) flows among multiple facilities, as occurring among different units in a hospital. Despite the immense queuing-theory literature, the model complexity of these features makes exact analysis far beyond the scope of existing methodologies. In response to these challenges, this research will develop optimal staffing strategies for large-scale queuing networks with all the above realistic features, aiming at uncovering fundamental principles, advancing operations research methodologies, devising effective control policies for better managing service systems, and seeking effective approximations that turn the large scale into an advantage instead of a disadvantage.

If successful, the results of this research will provide new tools to better design and manage service systems, with special emphasis on healthcare systems. To contain costs, many hospitals are significantly understaffed; when resources (such as nurses and beds) are inevitably limited, inefficient staffing strategies can cause excessive suffering, low quality care, degradation of the ultimate treatment outcomes, and significant increases in mortality. The proposed research intends to help system managers make the right operational decisions; it will help answer questions such as: How should a hospital staff the doctors and nurses over the course of the next week so that the delays before treatments are at most one hour? What percentage of patients will have to wait more than four hours? This research is also relevant for many other applications, for example, customer contact centers; public housing authorities providing apartments to low-income tenants; web-server farms processing requests for web pages; and financial back offices processing loans. Education outreach will be a key component of this project, because the investigator shares a deep commitment to engineering education at both the undergraduate and graduate levels. The investigator has been active in introducing operations research techniques (with a special emphasis on queuing theory) to K-12 students through a series of summer workshops and camps. The results of this project will be integrated into the investigator's new undergraduate and graduate courses.