generators: Convenience Functions for Generators

In Enchufa2/simmer: Discrete-Event Simulation for R

Convenience Functions for Generators

Description

These convenience functions facilitate the definition of generators of arrivals for some common cases.

Usage

at(...)

from(start_time, dist, arrive = TRUE)

to(stop_time, dist)

from_to(start_time, stop_time, dist, arrive = TRUE, every = NULL)

when_activated(n = 1)

Arguments

...	a vector or multiple parameters of times at which to initiate an arrival.
start_time	the time at which to launch the initial arrival (numeric or function).
dist	a function modelling the interarrival times. It is supposed to be an infinite source of values >= 0 (e.g., rexp and the like). If the function provided returns any negative value, the behaviour is undefined.
arrive	if set to TRUE (default) the first arrival will be generated at start_time and will follow dist from then on. If set to FALSE, will initiate dist at start_time (and the first arrival will most likely start at a time later than start_time).
stop_time	the time at which to stop the generator (numeric or function).
every	repeat with this time cycle (numeric or function).
n	an integer or a callable object (a function) which must return a number of arrivals to generate when activated.

Details

at generates arrivals at specific absolute times.

from generates inter-arrivals following a given distribution with a specified start time. union of the last two.

to generates inter-arrivals following a given distribution with a specified stop time.

from_to is the union of from and to.

when_activated sets up an initially inactive generator which generates n arrivals each time it is activated from any trajectory using the activity activate.

Value

Returns a generator function (a closure).

See Also

add_generator.

Examples

## common to all examples below
# some trajectory
t0 <- trajectory() %>%
  timeout(0)
# some distribution
distr <- function() runif(1, 1, 2)

# arrivals at 0, 1, 10, 30, 40 and 43
simmer() %>%
  add_generator("dummy", t0, at(0, c(1,10,30), 40, 43)) %>%
  run(100) %>%
  get_mon_arrivals()

# apply distribution starting at 5 (and no end)
simmer() %>%
  add_generator("dummy", t0, from(5, distr)) %>%
  run(10) %>%
  get_mon_arrivals()

# apply distribution until 5 (starting at 0)
simmer() %>%
  add_generator("dummy", t0, to(5, distr)) %>%
  run(10) %>%
  get_mon_arrivals()

# apply distribution from 8 to 16 h every 24 h:
simmer() %>%
  add_generator("dummy", t0, from_to(8, 16, distr, every=24)) %>%
  run(48) %>%
  get_mon_arrivals()

# triggering arrivals on demand from a trajectory
t1 <- trajectory() %>%
  activate("dummy")

simmer() %>%
  add_generator("dummy", t0, when_activated()) %>%
  add_generator("trigger", t1, at(2)) %>%
  run() %>%
  get_mon_arrivals()

Enchufa2/simmer documentation built on Dec. 9, 2023, 4:43 a.m.