Using the queuecomputer package

Anthony Ebert

2016-12-20

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 dataframe with a column of unique customer IDs labelled ‘ID’ and a column of arrival times labelled ‘times’. For example:

library(queuecomputer)

arrivals <- data.frame(ID = c(1:100), times = cumsum(rexp(100)))

head(arrivals)
##   ID     times
## 1  1 0.3199780
## 2  2 0.4630498
## 3  3 0.7251302
## 4  4 0.8995766
## 5  5 0.9654777
## 6  6 1.8800708
service <- rexp(100)

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

head(departures)
##   ID    times
## 1  1 0.580407
## 2  2 4.037948
## 3  3 4.622707
## 4  4 5.465278
## 5  5 6.134305
## 6  6 7.953326

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
## $x
## [1]  10  50 100
## 
## $y
## [1] 0 1 3 1
## 
## attr(,"class")
## [1] "list"           "server.stepfun"
departures <- queue_step(arrival_df = arrivals, service = service, servers = resource_schedule)

head(departures)
##   ID    times
## 1  1 10.26043
## 2  2 13.71797
## 3  3 14.30273
## 4  4 15.14530
## 5  5 15.81433
## 6  6 17.63335

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))
## [[1]]
## Step function
## Call: stats::stepfun(times[[i]], y)
##  x[1:1] =     10
## 2 plateau levels =      1,      0
## 
## [[2]]
## Step function
## Call: stats::stepfun(times[[i]], y)
##  x[1:2] =     15,     30
## 3 plateau levels =      0,      1,      0
## 
## [[3]]
## Step function
## Call: stats::stepfun(times[[i]], y)
##  x[1:1] =     10
## 2 plateau levels =      0,      1
## 
## attr(,"class")
## [1] "list"        "server.list"

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(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, union
set.seed(500)

arrivals <- data.frame(ID = c(1:100), times = cumsum(rexp(100)))
service_l <- rexp(100, 0.8)
service_q <- rexp(100, 0.5)
arrivals_b <- data.frame(ID = c(1:100), times = cumsum(rexp(100, 0.8)))

# The queue elements can be computed one by one. 

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

departure_df <- data.frame(arrival_times = arrivals$times, departures_1 = departures_1$times, departures_2 = departures_2$times, departures_3 = departures_3$times) %>% reshape2::melt()
## No id variables; using all as measure variables
qplot(value, data = departure_df, colour = variable, geom = "density") + xlab("time")

# 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$times)

all(departures$times == departures_3$times)
## [1] TRUE