Simulation-based Predictive Analytics for Dynamic Queueing Systems
Abstract
Simulation and simulation optimization have primarily been used for static system design problems based on long-run average performance measures. Control or policy-based optimization has been a weakness, because it requires a way to predict future behavior based on current state and time information. This work is a first step in that direction with a focus on congestion measures for queueing systems. The idea is to fit predictive models to dynamic sample paths of the system state from a detailed simulation. We propose a two-step method to dynamically predict the probability that the system state belongs to a certain subset and test the performance of this method on two examples.
Type
Publication
In 2017 Winter Simulation Conference (WSC), pp. 1716–1727
This paper was presented at the 2017 Winter Simulation Conference (WSC) and focuses on predictive analytics for dynamic queueing systems. The research introduces a novel method to dynamically predict the probability of system states based on simulation data, demonstrating its effectiveness on two examples.
