Trouble Ticket Routing Models and Their Applications

Abstract

A trouble ticket is an important information carrier in system maintenance, which records problem symptoms, the resolving process, and resolutions. A critical challenge for the ticket management system is how to quickly deal with trouble tickets and fix problems. Thousands of tickets, bouncing among multiple expert groups before being fixed, will consume limited system maintenance resources and may also violate the service level agreement. Thus, trouble tickets should be routed to the right expert group as quickly as possible in order to reduce the processing delay. In this paper, to address the challenge in ticket routing, we exploit three different routing models by mining the combination of problem descriptions and resolution sequences from the historical resolved tickets, and develop the corresponding routing recommendation algorithms to determine the next expert group to solve the problem. To evaluate the performance of routing recommendation algorithms, we conduct extensive experiments on a real ticket data set. The experimental results show that the proposed models and algorithm can effectively shorten the mean number of steps to resolve with a high ratio of the number of successfully resolved tickets to the total number of tickets, especially for the long routing sequences generated from manual assignments. These models and algorithms have the potential of being used in a ticket routing recommendation engine to greatly reduce human intervention in the routing process.