The Approaches Utilized in Queuing Modeling: A Systematic Literature Review

Abstract

Queue scheduling is an important process that is used when processes can be divided into different classes based on the scheduling needs. In general, queue lengths and waiting times can be predicted with the use of a queuing model; especially, the importance of the services provided by any sector or organization for delivering effective services to their consumers highly depends on managing the queues in an effective manner. So, in general, analyzing and evaluating queues is more significant in fulfilling customer needs in a limited time. The applications and tactics developed earlier would not provide satisfactory solutions to solve this problem. But the studies done in the recent past have utilized newer technologies. Especially by incorporating Machine Learning techniques and various queuing models. During this study, a thorough analysis was carried out to discover the different strategies used in optimizing the queues by presenting a Systematic Literature Review (SLR) by examining available research perspective to queue management systems from 2016 to 2022. Initially 200 studies selected using seven electronic repositories and finally selected 14 for this analysis. The study's findings reveals that most of the studies have used machine learning approaches, utilizing tools like ARENA,SIMIO and adapting various queuing algorithms to solve this problem.