wait_step: Compute maximum time for each row from two vectors of arrival times.

Description

Compute maximum time for each row from two vectors of arrival times.

Usage

wait_step(arrivals, service)

Value

The maximum time from two vectors of arrival times.

Arguments

arrivals: Either a numeric vector or an object of class queue_list. It represents the arrival times.
service: A vector of times which represent the arrival times of the second type of customers. The ordering of this vector should have the same ordering as arrivals.

Details

A good real-world example of this is finding the departure times for passengers after they pick up their bags from the baggage carousel. The time at which they leave is the maximum of the passenger and bag arrival times.

Examples

Run this code

set.seed(500)
arrivals <- rlnorm(100, meanlog = 4)
service <- rlnorm(100)

#Airport example ------------------------

# Create a number of bags for each of 100 customers
bags <- rpois(100,1)

# Create a bags dataframe, with each bag associated with one customer.
bags.df <- data.frame(BagID = 1:sum(bags),
   ID = rep(1:100, bags), times = rlnorm(sum(bags), meanlog = 2))

# Create a function which will return the maximum time from each customer's set of bags.

reduce_bags <- function(bagdataset, number_of_passengers){
   ID = NULL
   times = NULL

   zerobags <- data.frame(BagID = NA, ID = c(1:number_of_passengers), times = 0)
   reduced_df <- as.data.frame(dplyr::summarise(dplyr::group_by(
   rbind(bagdataset, zerobags), ID), n = max(times, 0)))
   ord <- order(reduced_df$ID)
   reduced_df <- reduced_df[order(ord),]
   names(reduced_df) <- c("ID", "times")
   return(reduced_df)
}


arrivals2 <- reduce_bags(bags.df, 100)$times

# Find the time when customers can leave with their bags.
wait_step(arrivals = arrivals, service = arrivals2)