Implementation of a computationally efficient method for
simulating queues with arbitrary arrival and service times.
Please see Ebert, Wu, Mengersen & Ruggeri (2020,
queuecomputer implements a new and computationally efficient method for simulating from a general set of queues. The current most popular method for simulating queues is Discete Event Simulation (DES). The top R package for DES is called simmer and the top Python package is called SimPy. We have validated and benchmarked queuecomputer against both these packages and found that queuecomputer is two orders of magnitude faster than either package.
Simulating arbitrary queues is difficult, however once:
then the departure times D for all customers can be computed deterministically.
The focus on this package is:
It is up to the user to provide arrival and service times, and therefore very complicated distributions can be simulated (by the user) and tested with this package.
For detailed information regarding the algorithm used in this package see our arXiv preprint:
Ebert, A., Wu, P., Mengersen, K., & Ruggeri, F. (2017). Computationally Efficient Simulation of Queues: The R Package queuecomputer. arXiv preprint arXiv:1703.02151.
In this example of a queueing network, customers must pass through two queues. The arrival times to the first queue come in two waves starting at time 100 and time 500. The arrival times to the second queue are the departure times of the first queue plus the time they spent walking to the second queue.
library(queuecomputer)library(ggplot2)library(dplyr)#>#> Attaching package: 'dplyr'#> The following objects are masked from 'package:stats':#>#> filter, lag#> The following objects are masked from 'package:base':#>#> intersect, setdiff, setequal, unionset.seed(1)n <- 100arrivals_1 <- c(100 + cumsum(rexp(n)), 500 + cumsum(rexp(n)))service_1 <- rexp(2*n, 1/2.5)queue_1 <- queue_step(arrivals = arrivals_1, service = service_1, servers = 2)walktimes <- rexp(2*n, 1/100)arrivals_2 <- lag_step(arrivals = queue_1, service = walktimes)service_2 <- rexp(2*n, 1/3)queue_2 <- queue_step(arrivals = arrivals_2, service = service_2, servers = 1)head(arrivals_1)#>  100.7552 101.9368 102.0825 102.2223 102.6584 105.5534head(queue_1$departures_df)#> # A tibble: 6 x 6#> arrivals service departures waiting system_time server#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>#> 1 101. 0.189 101. -6.38e-15 0.189 1#> 2 102. 2.57 105. 4.44e-15 2.57 2#> 3 102. 1.69 104. 0. 1.69 1#> 4 102. 2.00 106. 1.55e+ 0 3.55 1#> 5 103. 0.435 105. 1.84e+ 0 2.28 2#> 6 106. 1.68 107. 0. 1.68 2head(arrivals_2)#>  120.3923 105.6711 227.5242 175.9008 339.9853 108.7119head(queue_2$departures_df)#> # A tibble: 6 x 6#> arrivals service departures waiting system_time server#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>#> 1 120. 5.16 126. -2.66e-15 5.16 1#> 2 106. 1.58 107. 0. 1.58 1#> 3 228. 0.114 291. 6.32e+ 1 63.3 1#> 4 176. 2.35 186. 8.02e+ 0 10.4 1#> 5 340. 3.20 404. 6.13e+ 1 64.5 1#> 6 109. 1.23 110. 0. 1.23 1summary(queue_1)#> Total customers:#> 200#> Missed customers:#> 0#> Mean waiting time:#> 11.4#> Mean response time:#> 13.7#> Utilization factor:#> 0.38410206651912#> Mean queue length:#> 3.7#> Mean number of customers in system:#> 4.46summary(queue_2)#> Total customers:#> 200#> Missed customers:#> 0#> Mean waiting time:#> 34.1#> Mean response time:#> 37.2#> Utilization factor:#> 0.519160307775743#> Mean queue length:#> 5.71#> Mean number of customers in system:#> 6.21
This work was supported by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). This work was funded through the ARC Linkage Grant “Improving the Productivity and Efficiency of Australian Airports” (LP140100282).
statelist element from queue_step output
queue_step()is faster. The speed of
queue_stepno longer errors if arrivals is an integer vector
summary.queue_listnow returns a warning rather than an error if a
server.listobject is inputted.
create_batchesto easily create batch arrival systems.
"radix"method within the
queuefunction for high level users
as.server.stepfunnow checks if departure times force move to next epoch. Previously (in some cases) new customers could be served by servers in previous epochs.
as.server.stepfunfor correct input.