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QLess provides queue management software that aims to eliminate crowded lobbies and give people back their time from waiting in line. The software uses a combination of online, kiosk, and mobile queuing techniques which allows people to join a line from different access points and wait remotely instead of needing to be on-location and in the lobby.
Example of a ticket and "Now Serving" sign used in the Ticket Queue Management System. The basic concept of a ticket lock is similar to the ticket queue management system. This is the method that many bakeries and delis use to serve customers in the order that they arrive, without making them stand in a line.
The efficiency of queueing systems is gauged through key performance metrics. These include the average queue length, average wait time, and system throughput. These metrics provide insights into the system's functionality, guiding decisions aimed at enhancing performance and reducing wait times. References: Gross, D., & Harris, C. M. (1998).
Pages in category "Queue management" The following 16 pages are in this category, out of 16 total. This list may not reflect recent changes. C. Cutting in line; F.
A “virtual placeholder” maintains the customers' position in the queue while the ACD queue is worked off. The virtual queuing system monitors the rate at which calls in queue are worked off and launches an outbound call to the customer moments before the virtual placeholder is due to reach the top of the queue.
An active queue management and denial-of-Service (AQM&DoS) simulation platform is established based on the NS-2 simulation code of the RRED algorithm. The AQM&DoS simulation platform can simulate a variety of DoS attacks (Distributed DoS, Spoofing DoS, Low-rate DoS, etc.) and AQM algorithms (RED, RRED, SFB, etc.). It automatically calculates ...
The Queuing Rule of Thumb assists queue management to resolve queue problems by relating the number of servers, the total number of customers, the service time, and the maximum time needed to finish the queue. To make a queuing system more efficient, these values can be adjusted with regards to the rule of thumb. [3]
M/M/1 queue: M Y: bulk Markov: Exponential service time with a random variable Y for the size of the batch of entities serviced at one time. M X /M Y /1 queue: D: Degenerate distribution: A deterministic or fixed service time. M/D/1 queue: E k: Erlang distribution: An Erlang distribution with k as the shape parameter (i.e., sum of k i.i.d ...
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