Title:  QPLEX:  A next-generation methodology for stochastic network analysis

Abstract:  QPLEX is a nonstationary, nonparametric, non-Markovian modeling and analysis paradigm for stochastic networks. QPLEX generates transient distributions of key performance metrics, such as the number of customers and the virtual waiting time at each station at all times. From a modeling perspective, QPLEX is quite versatile: for example, it can accommodate time-varying arrival processes, arbitrary time-varying service-time distributions, time-varying server counts, abandonments, balking, probabilistic routing and capacity/routing policies based on cycle-time distributions.

In this talk, we will present a proof-of-concept that is remarkably accurate, widely applicable, and extremely fast. We will describe the mechanisms underlying the QPLEX calculus as well as the principles of QPLEX modeling. We will also discuss future directions and prospective application areas.  This is joint work with Steve Hackman (Georgia Tech).

Bio: Ton Dieker is Associate Professor of Industrial Engineering and Operations Research at Columbia University, and a member of Columbia’s Data Science Institute. He received an M.Sc. from Vrije Universiteit Amsterdam (2002) and a Ph.D. from University of Amsterdam (2006). Prior to joining Columbia, he was the Fouts Family Associate Professor at Georgia Tech. Honors and awards include the Goldstine Fellowship from IBM Research, the Erlang Prize from the INFORMS Applied Probability Society, and a PECASE Award from the White House. He serves/has served on the editorial board of several journals in Operations Research and Applied Probability.