2019/12/17 by Majid Raeis, Raeis, Majid, Ali Tizghadam +4
Business, Management and Accounting · Computer Science · Social Sciences · #Advanced Queuing Theory Analysis #FOS: Computer and information sciences #Performance (cs.PF) #Transportation Planning and Optimization #Wireless Communication Networks Research #cs.PF
paper · pdf · doi:10.48550/arxiv.1912.08368
openalex publication_date 2019/12/17 · arxiv created 2019/12/18 · arxiv updated 2019/12/19 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Motivated by interest in providing more efficient services in customer service systems, we use statistical learning methods and delay history information to predict the conditional distribution of the customers' waiting times in queueing systems. From the predicted distributions, descriptive statistics of the system such as the mean, variance and percentiles of the waiting times can be obtained, which can be used for delay announcements, SLA conformance and better system management. We model the conditional distributions by mixtures of Gaussians, parameters of which can be estimated using Mixture Density Networks. The evaluations show that exploiting more delay history information can result in much more accurate predictions under realistic time-varying arrival assumptions.