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.,rexpand 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 atstart_timeand will followdistfrom then on. If set toFALSE, will initiatedistatstart_time(and the first arrival will most likely start at a time later thanstart_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.
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()
#> name start_time end_time activity_time finished replication
#> 1 dummy0 0 0 0 TRUE 1
#> 2 dummy1 1 1 0 TRUE 1
#> 3 dummy2 10 10 0 TRUE 1
#> 4 dummy3 30 30 0 TRUE 1
#> 5 dummy4 40 40 0 TRUE 1
#> 6 dummy5 43 43 0 TRUE 1
# apply distribution starting at 5 (and no end)
simmer() %>%
add_generator("dummy", t0, from(5, distr)) %>%
run(10) %>%
get_mon_arrivals()
#> name start_time end_time activity_time finished replication
#> 1 dummy0 5.000000 5.000000 0 TRUE 1
#> 2 dummy1 6.054850 6.054850 0 TRUE 1
#> 3 dummy2 7.625511 7.625511 0 TRUE 1
#> 4 dummy3 9.555658 9.555658 0 TRUE 1
# apply distribution until 5 (starting at 0)
simmer() %>%
add_generator("dummy", t0, to(5, distr)) %>%
run(10) %>%
get_mon_arrivals()
#> name start_time end_time activity_time finished replication
#> 1 dummy0 1.543105 1.543105 0 TRUE 1
#> 2 dummy1 3.008031 3.008031 0 TRUE 1
#> 3 dummy2 4.293720 4.293720 0 TRUE 1
# 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()
#> name start_time end_time activity_time finished replication
#> 1 dummy0 8.000000 8.000000 0 TRUE 1
#> 2 dummy1 9.349974 9.349974 0 TRUE 1
#> 3 dummy2 10.927960 10.927960 0 TRUE 1
#> 4 dummy3 12.649415 12.649415 0 TRUE 1
#> 5 dummy4 14.024923 14.024923 0 TRUE 1
#> 6 dummy5 15.775842 15.775842 0 TRUE 1
#> 7 dummy6 32.000000 32.000000 0 TRUE 1
#> 8 dummy7 33.043767 33.043767 0 TRUE 1
#> 9 dummy8 34.364540 34.364540 0 TRUE 1
#> 10 dummy9 35.684818 35.684818 0 TRUE 1
#> 11 dummy10 37.669201 37.669201 0 TRUE 1
#> 12 dummy11 39.175569 39.175569 0 TRUE 1
# 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()
#> name start_time end_time activity_time finished replication
#> 1 trigger0 2 2 0 TRUE 1
#> 2 dummy0 2 2 0 TRUE 1