Abstract: Delay or queue length information has the potential to influence the decision of a customer to join a queue. Therefore, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. In this talk, we will analyze two two-dimensional deterministic fluid models that incorporate customer choice behavior based on delayed queue length information. In the first fluid model, customers join each queue according to a Multinomial Logit Choice Model, however, the queue length information the customer receives is delayed by a constant amount of time which we call the delay. We show that oscillations or asynchronous behavior in the queueing model can occur based on the size of the delay. In the second model, customers receive information about the queue length through a moving average of the queue length. Although it has been shown empirically that giving patients moving average information causes oscillations and asynchronous behavior to occur in U.S. hospitals. We will also show that the moving average fluid model can exhibit oscillations and determine its dependence on the moving average window. Thus, our analysis provides new insight on how managers of queueing systems should report queue length information to customers and how delayed information can produce unwanted behavior.
Biography: Jamol Pender is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University. Professor Pender received his BSE/MSE in Electrical and Systems Engineering at the University of Pennsylvania. In 2008 and his PhD from Princeton University in 2013. He was a postdoctoral fellow at Columbia University before joining the faculty at Cornell in 2015. His research is in the area of applied probability where he is interested in studying queues with time varying rates. Queues with time varying rates are more complicated than their constant rate counterparts and therefore, his research attempts to find low dimensional approximations that describe the sample path behavior of these time varying queueing systems. His research is also applied in a variety areas such as telecommunication networks, healthcare, transportation systems, and more recently "nightlife systems" such as clubs.