First, load the package and instantiate a new simulation environment.
library(simmer)
set.seed(42)
<- simmer("SuperDuperSim")
env
env#> simmer environment: SuperDuperSim | now: 0 | next:
#> { Monitor: in memory }
Set-up a simple trajectory. Let’s say we want to simulate an ambulatory consultation where a patient is first seen by a nurse for an intake, next by a doctor for the consultation and finally by administrative staff to schedule a follow-up appointment.
<- trajectory("patients' path") %>%
patient ## add an intake activity
seize("nurse", 1) %>%
timeout(function() rnorm(1, 15)) %>%
release("nurse", 1) %>%
## add a consultation activity
seize("doctor", 1) %>%
timeout(function() rnorm(1, 20)) %>%
release("doctor", 1) %>%
## add a planning activity
seize("administration", 1) %>%
timeout(function() rnorm(1, 5)) %>%
release("administration", 1)
In this case, the argument of the timeout
activity is a
function, which is evaluated dynamically to produce a stochastic waiting
time, but it could be a constant too. Apart from that, this function may
be as complex as you need and may do whatever you want: interact with
entities in your simulation model, get resources’ status, make decisions
according to the latter…
Once the trajectory is known, you may attach arrivals to it and
define the resources needed. In the example below, three types of
resources are added: the nurse and administration
resources, each one with a capacity of 1, and the doctor
resource, with a capacity of 2. The last method adds a generator of
arrivals (patients) following the trajectory patient
. The
time between patients is about 10 minutes (a Gaussian of
mean=10
and sd=2
). (Note: returning a negative
interarrival time at some point would stop the generator).
%>%
env add_resource("nurse", 1) %>%
add_resource("doctor", 2) %>%
add_resource("administration", 1) %>%
add_generator("patient", patient, function() rnorm(1, 10, 2))
#> simmer environment: SuperDuperSim | now: 0 | next: 0
#> { Monitor: in memory }
#> { Resource: nurse | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Resource: doctor | monitored: TRUE | server status: 0(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Source: patient | monitored: 1 | n_generated: 0 }
The simulation is now ready for a test run; just let it
simmer for a bit. Below, we specify that we want to limit the
runtime to 80 time units using the until
argument. After
that, we verify the current simulation time (now
) and when
will be the next 3 events (peek
).
%>%
env run(80) %>%
now()
#> [1] 80
%>% peek(3)
env #> [1] 80.69540 81.62105 81.62105
It is possible to run the simulation step by step, and such a method is chainable too.
%>%
env stepn() %>% # 1 step
print() %>%
stepn(3) # 3 steps
#> simmer environment: SuperDuperSim | now: 80.6953988949657 | next: 80.6953988949657
#> { Monitor: in memory }
#> { Resource: nurse | monitored: TRUE | server status: 1(1) | queue status: 1(Inf) }
#> { Resource: doctor | monitored: TRUE | server status: 1(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Source: patient | monitored: 1 | n_generated: 7 }
#> simmer environment: SuperDuperSim | now: 81.6210531397386 | next: 81.6210531397386
#> { Monitor: in memory }
#> { Resource: nurse | monitored: TRUE | server status: 1(1) | queue status: 2(Inf) }
#> { Resource: doctor | monitored: TRUE | server status: 1(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: TRUE | server status: 0(1) | queue status: 0(Inf) }
#> { Source: patient | monitored: 1 | n_generated: 7 }
%>% peek(Inf, verbose=TRUE)
env #> time process
#> 1 81.62105 patient
#> 2 86.74154 patient4
#> 3 89.36934 patient3
Also, it is possible to resume the automatic execution simply by specifying a longer runtime. Below, we continue the execution until 120 time units.
%>%
env run(120) %>%
now()
#> [1] 120
You can also reset the simulation, flush all results, resources and generators, and restart from the beginning.
%>%
env reset() %>%
run(80) %>%
now()
#> [1] 80
It is very easy to replicate a simulation multiple times using standard R functions.
<- lapply(1:100, function(i) {
envs simmer("SuperDuperSim") %>%
add_resource("nurse", 1) %>%
add_resource("doctor", 2) %>%
add_resource("administration", 1) %>%
add_generator("patient", patient, function() rnorm(1, 10, 2)) %>%
run(80)
})
The advantage of the latter approach is that, if the individual
replicas are heavy, it is straightforward to parallelise their execution
(for instance, in the next example we use the function
mclapply
from the parallel)
package. However, the external pointers to the C++ simmer core are no
longer valid when the parallelised execution ends. Thus, it is necessary
to extract the results for each thread at the end of the execution. This
can be done with the helper function wrap
as follows.
library(parallel)
<- mclapply(1:100, function(i) {
envs simmer("SuperDuperSim") %>%
add_resource("nurse", 1) %>%
add_resource("doctor", 2) %>%
add_resource("administration", 1) %>%
add_generator("patient", patient, function() rnorm(1, 10, 2)) %>%
run(80) %>%
wrap()
})
This helper function brings the simulation data back to R and makes
it accessible through the same methods that would ordinarily be used for
a simmer
environment.
1]] %>% get_n_generated("patient")
envs[[#> [1] 10
1]] %>% get_queue_count("doctor")
envs[[#> [1] 0
1]] %>% get_queue_size("doctor")
envs[[#> [1] Inf
%>%
envs get_mon_resources() %>%
head()
#> resource time server queue capacity queue_size system limit replication
#> 1 nurse 8.685009 1 0 1 Inf 1 Inf 1
#> 2 nurse 19.369237 1 1 1 Inf 2 Inf 1
#> 3 nurse 25.454124 1 0 1 Inf 1 Inf 1
#> 4 doctor 25.454124 1 0 2 Inf 1 Inf 1
#> 5 nurse 26.576981 1 1 1 Inf 2 Inf 1
#> 6 nurse 37.359567 1 2 1 Inf 3 Inf 1
%>%
envs get_mon_arrivals() %>%
head()
#> name start_time end_time activity_time finished replication
#> 1 patient0 8.685009 50.28543 41.60042 TRUE 1
#> 2 patient1 19.369237 64.76070 39.30657 TRUE 1
#> 3 patient0 6.904085 46.20556 39.30147 TRUE 2
#> 4 patient1 19.258465 59.96414 37.32483 TRUE 2
#> 5 patient2 29.510899 75.52605 39.56584 TRUE 2
#> 6 patient0 9.679566 53.53968 43.86011 TRUE 3
Unfortunately, as the C++ simulation cores are destroyed, the downside of this kind of parallelization is that one cannot resume execution of the replicas.
You may want to try the simmer.plot
package, a plugin
for simmer
that provides some basic visualisation tools to
help you take a quick glance at your simulation results or debug a
trajectory object: