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: 1 | server status: 0(1) | queue status: 0(Inf) }
#> { Resource: doctor | monitored: 1 | server status: 0(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: 1 | 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: 85.2188854357887
#> { Resource: nurse | monitored: 1 | server status: 1(1) | queue status: 2(Inf) }
#> { Resource: doctor | monitored: 1 | server status: 1(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: 1 | server status: 0(1) | queue status: 0(Inf) }
#> { Generator: patient | monitored: 1 | n_generated: 8 }
env %>% now()
#> [1] 80
env %>% peek(3)
#> [1] 85.21889 87.59856 87.59856

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

env %>% onestep()
#> simmer environment: SuperDuperSim | now: 85.2188854357887 | next: 85.2188854357887
#> { Resource: nurse | monitored: 1 | server status: 0(1) | queue status: 2(Inf) }
#> { Resource: doctor | monitored: 1 | server status: 1(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: 1 | server status: 0(1) | queue status: 0(Inf) }
#> { Generator: patient | monitored: 1 | n_generated: 8 }
env %>% onestep() %>% onestep() %>% onestep()
#> simmer environment: SuperDuperSim | now: 85.2188854357887 | next: 85.2188854357887
#> { Resource: nurse | monitored: 1 | server status: 1(1) | queue status: 1(Inf) }
#> { Resource: doctor | monitored: 1 | server status: 2(2) | queue status: 0(Inf) }
#> { Resource: administration | monitored: 1 | server status: 0(1) | queue status: 0(Inf) }
#> { Generator: patient | monitored: 1 | n_generated: 8 }
env %>% now()
#> [1] 85.21889
env %>% peek(Inf, verbose=TRUE)
#>        time  process
#> 1  85.21889 patient5
#> 2  87.59856  patient
#> 3  87.59856 patient7
#> 4  90.10474 patient3
#> 5 105.14006 patient4

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] 8
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 10.29061      1     0        1        Inf      1   Inf
#> 2    nurse 19.27380      1     1        1        Inf      2   Inf
#> 3    nurse 25.27724      1     0        1        Inf      1   Inf
#> 4   doctor 25.27724      1     0        2        Inf      1   Inf
#> 5    nurse 30.71322      1     1        1        Inf      2   Inf
#> 6    nurse 39.38950      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   10.29061 51.43398      41.14338     TRUE           1
#> 2 patient1   19.27380 63.27818      38.00094     TRUE           1
#> 3 patient2   30.71322 79.25345      39.49035     TRUE           1
#> 4 patient0   10.42315 51.42190      40.99875     TRUE           2
#> 5 patient1   23.90291 66.27801      41.68423     TRUE           2
#> 6 patient0   10.15652 49.60249      39.44597     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: