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 6.080750 6.080750 0 TRUE 1
#> 3 dummy2 7.915083 7.915083 0 TRUE 1
#> 4 dummy3 9.515844 9.515844 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 1.007399 1.007399 0 TRUE 1
#> 2 dummy1 2.473793 2.473793 0 TRUE 1
#> 3 dummy2 3.971570 3.971570 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 9.732882 9.732882 0 TRUE 1
#> 3 dummy2 11.505403 11.505403 0 TRUE 1
#> 4 dummy3 13.380004 13.380004 0 TRUE 1
#> 5 dummy4 14.554945 14.554945 0 TRUE 1
#> 6 dummy5 15.589186 15.589186 0 TRUE 1
#> 7 dummy6 32.000000 32.000000 0 TRUE 1
#> 8 dummy7 33.402328 33.402328 0 TRUE 1
#> 9 dummy8 34.597998 34.597998 0 TRUE 1
#> 10 dummy9 36.001536 36.001536 0 TRUE 1
#> 11 dummy10 37.065198 37.065198 0 TRUE 1
#> 12 dummy11 38.453899 38.453899 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