-- It corresponds to model MachRep3 described in document
-- Introduction to Discrete-Event Simulation and the SimPy Language
-- [http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf].
-- SimPy is available on [http://simpy.sourceforge.net/].
--
-- The model description is as follows.
--
-- Variation of models MachRep1, MachRep2. Two machines, but
-- sometimes break down. Up time is exponentially distributed with mean
-- 1.0, and repair time is exponentially distributed with mean 0.5. In
-- this example, there is only one repairperson, and she is not summoned
-- until both machines are down. We find the proportion of up time. It
-- should come out to about 0.45.
module MachRep3Model (model) where
import System.Random
import Control.Monad
import Control.Monad.Trans
import Simulation.Aivika.Dynamics
import Simulation.Aivika.Dynamics.Simulation
import Simulation.Aivika.Dynamics.Base
import Simulation.Aivika.Dynamics.EventQueue
import Simulation.Aivika.Dynamics.Ref
import Simulation.Aivika.Dynamics.Resource
import Simulation.Aivika.Dynamics.Process
import Simulation.Aivika.Experiment
import Simulation.Aivika.Experiment.LastValueView
import Simulation.Aivika.Experiment.TableView
import Simulation.Aivika.Experiment.TimeSeriesView
import Simulation.Aivika.Experiment.DeviationChartView
specs = Specs { spcStartTime = 0.0,
spcStopTime = 1000.0,
spcDT = 1.0,
spcMethod = RungeKutta4 }
experiment :: Experiment
experiment =
defaultExperiment {
experimentSpecs = specs,
experimentRunCount = 3,
experimentDescription = "Experiment Description",
experimentGenerators =
[outputView $ defaultLastValueView {
lastValueDescription = "Last Value description",
lastValueSeries = ["x"] },
outputView $ defaultTableView {
tableDescription = "Table description",
tableSeries = ["x"] },
outputView $ defaultTimeSeriesView {
timeSeries = [Left "t", Right "x"] },
outputView $ defaultDeviationChartView {
deviationChartSeries = [Left "t", Right "x"] } ] }
upRate = 1.0 / 1.0 -- reciprocal of mean up time
repairRate = 1.0 / 0.5 -- reciprocal of mean repair time
exprnd :: Double -> IO Double
exprnd lambda =
do x <- getStdRandom random
return (- log x / lambda)
model :: Simulation ExperimentData
model =
do queue <- newQueue
-- number of machines currently up
nUp <- newRef queue 2
-- total up time for all machines
totalUpTime <- newRef queue 0.0
repairPerson <- newResource queue 1
pid1 <- newProcessID queue
pid2 <- newProcessID queue
let machine :: ProcessID -> Process ()
machine pid =
do startUpTime <- liftDynamics time
upTime <- liftIO $ exprnd upRate
holdProcess upTime
finishUpTime <- liftDynamics time
liftDynamics $ modifyRef totalUpTime
(+ (finishUpTime - startUpTime))
liftDynamics $ modifyRef nUp $ \a -> a - 1
nUp' <- liftDynamics $ readRef nUp
if nUp' == 1
then passivateProcess
else do n <- liftDynamics $
resourceCount repairPerson
when (n == 1) $
liftDynamics $ reactivateProcess pid
requestResource repairPerson
repairTime <- liftIO $ exprnd repairRate
holdProcess repairTime
liftDynamics $ modifyRef nUp $ \a -> a + 1
releaseResource repairPerson
machine pid
runDynamicsInStartTime $
do t0 <- starttime
runProcess (machine pid2) pid1 t0
runProcess (machine pid1) pid2 t0
let result =
do x <- readRef totalUpTime
y <- time
return $ x / (2 * y)
experimentDataInStartTime queue $
[("x", seriesEntity "The proportion of up time" result),
("t", seriesEntity "Total up time" totalUpTime)]
main = runExperiment experiment model