Using the queuecomputer package"

Introduction

The purpose of the package queuecomputer is to compute, deterministically, the output of a queue network given the arrival and service times for all customers. The most important functions are queue_step, lag_step and wait_step.

Input format

The first argument to the functions queue_step, lag_step and wait_step is a vector of arrival times. For example:

library(queuecomputer)
library(dplyr)

arrivals <- cumsum(rexp(100))

head(arrivals)

service <- rexp(100)

departures <- queue_step(arrivals = arrivals, service = service)

str(departures,1)

Resourcing schedule

The resourcing schedule is specified with either a non-zero natural number, a server.stepfun or a server.list object. Use a non-zero natural number when the number of servers does not change over time. The server.stepfun specifies a step function to indicate how many servers are available throughout the day. The computation speed for queue_step() is much faster when using a server.stepfun rather than a server.list input for the servers argument.

We create a server.stepfun object with the as.server.stepfun function.

# Zero servers available before time 10
# One server available between time 10 and time 50
# Three servers available between time 50 and time 100
# One server available from time 100 onwards
resource_schedule <- as.server.stepfun(c(10,50,100), c(0, 1, 3, 1))

resource_schedule

departures <- queue_step(arrivals = arrivals, service = service, servers = resource_schedule)

str(departures,1)

The server.list object is a list of step functions which represent each server, the range is ${0,1}$, where 0 represents unavailable and 1 represents available and the knots represent the times where availability changes.

The as.server.list function is used to create a server.list object.

# Server 1 is available before time 10.
# Server 2 is available between time 15 and time 30.
# Server 3 is available after time 10. 
as.server.list(list(10, c(15,30), 10), c(1,0,0))

Setting up a queue network

It is simple to set up a chain of queueing elements with queuecomputer. Suppose passengers must walk to a queue, then wait for service and then wait for their bags.

library(queuecomputer)
library(dplyr)

set.seed(500)

n <- 100

arrivals <- cumsum(rexp(n))
service_l <- rexp(n, 0.8)
service_q <- rexp(n, 0.5)
arrivals_b <- cumsum(rexp(n, 0.8))

# The queue elements can be computed one by one. 

departures_1 <- lag_step(arrivals, service_l)
departures_2 <- queue(departures_1, service = service_q, servers = 2)
departures_3 <- wait_step(departures_2, arrivals_b)

# Or the queue elements can be chained together with the %>% operator. 

departures <- lag_step(arrivals, service_l) %>% queue_step(service = service_q, servers = 2) %>% wait_step(arrivals_b)

all(departures == departures_3)

# Plot densities for this tandem queueing network

colours <- rainbow(4)
plot(density(arrivals, from = 0), 
  col = colours[1], xlim = c(0, 220), ylim = c(0, 0.015), 
  main = "Density plot")
lines(density(departures_1, from = 0), col = colours[2])
lines(density(departures_2, from = 0), col = colours[3])
lines(density(departures_3, from = 0), col = colours[4])
legend(150,0.012, legend = c("Start walk",
    "Finish walk",
    "Finish service", 
    "Pick up bag"),
    col = colours, lwd = 1, cex = 0.8
)


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queuecomputer documentation built on Nov. 16, 2022, 1:07 a.m.