Basic usage

First, load the package and instantiate a new simulation environment.

library(simmer)

env <- simmer("SuperDuperSim")
env
#> simmer environment: SuperDuperSim | now: 0 | next:

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.

patient <- trajectory("patients' path") %>%
  ## 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 an 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
#> { 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) }
#> { Generator: 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(until=80)
#> simmer environment: SuperDuperSim | now: 80 | next: 80.9343987305476
#> { Resource: nurse | monitored: TRUE | server status: 1(1) | queue status: 3(Inf) }
#> { Resource: doctor | monitored: TRUE | server status: 1(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: TRUE | server status: 1(1) | queue status: 0(Inf) }
#> { Generator: patient | monitored: 1 | n_generated: 9 }
env %>% now()
#> [1] 80
env %>% peek(3)
#> [1] 80.93440 83.20876 83.20876

It is possible to run the simulation step by step, and such a method is chainable too.

env %>% onestep()
#> simmer environment: SuperDuperSim | now: 80.9343987305476 | next: 80.9343987305476
#> { Resource: nurse | monitored: TRUE | server status: 1(1) | queue status: 3(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) }
#> { Generator: patient | monitored: 1 | n_generated: 9 }
env %>% onestep() %>% onestep() %>% onestep()
#> simmer environment: SuperDuperSim | now: 83.2087644922746 | next: 83.2087644922746
#> { Resource: nurse | monitored: TRUE | server status: 1(1) | queue status: 3(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) }
#> { Generator: patient | monitored: 1 | n_generated: 10 }
env %>% now()
#> [1] 83.20876
env %>% peek(Inf, verbose=TRUE)
#>       time  process
#> 1 83.20876 patient8
#> 2 86.05747 patient4
#> 3 92.69879 patient3
#> 4 95.12703  patient
#> 5 95.12703 patient9

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(until=120) %>%
  now()
#> [1] 120

Finally, you can reset the simulation, flush all results, resources and generators, and restart from the beginning.

env %>% 
  reset() %>% 
  run(until=80) %>%
  now()
#> [1] 80

Replication

It is very easy to replicate a simulation multiple times using standard R functions.

envs <- lapply(1:100, function(i) {
  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 parallelize their execution (for instance, in the next example we use the function mclapply from the package parallel). However, the external pointers to the C++ simmer core are no longer valid when the parallelized 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)

envs <- mclapply(1:100, function(i) {
  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.

envs[[1]] %>% get_n_generated("patient")
#> [1] 9
envs[[1]] %>% get_capacity("doctor")
#> [1] 2
envs[[1]] %>% get_queue_size("doctor")
#> [1] Inf
head(
  envs %>% get_mon_resources()
)
#>   resource     time server queue capacity queue_size system limit
#> 1    nurse 13.21027      1     0        1        Inf      1   Inf
#> 2    nurse 20.86423      1     1        1        Inf      2   Inf
#> 3    nurse 29.88650      1     2        1        Inf      3   Inf
#> 4    nurse 29.98237      1     1        1        Inf      2   Inf
#> 5   doctor 29.98237      1     0        2        Inf      1   Inf
#> 6    nurse 40.40987      1     2        1        Inf      3   Inf
#>   replication
#> 1           1
#> 2           1
#> 3           1
#> 4           1
#> 5           1
#> 6           1
head(
  envs %>% get_mon_arrivals()
)
#>       name start_time end_time activity_time finished replication
#> 1 patient0  13.210269 56.51859      43.30832     TRUE           1
#> 2 patient1  20.864235 71.73536      41.75299     TRUE           1
#> 3 patient0  11.337218 48.98983      37.65261     TRUE           2
#> 4 patient1  17.113941 68.03850      41.60461     TRUE           2
#> 5 patient0   7.779879 48.66799      40.88811     TRUE           3
#> 6 patient1  16.500845 60.64469      37.71702     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.

Basic visualisation tools

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: