aivika-lattice-0.1: tests/EstimatingMachRep1.hs
-- It corresponds to model MachRep1 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.
--
-- Two machines, which sometimes break down.
-- Up time is exponentially distributed with mean 1.0, and repair time is
-- exponentially distributed with mean 0.5. There are two repairpersons,
-- so the two machines can be repaired simultaneously if they are down
-- at the same time.
--
-- Output is long-run proportion of up time. Should get value of about
-- 0.66.
import Control.Monad
import Control.Monad.Trans
import Simulation.Aivika.Trans
import Simulation.Aivika.Lattice
meanUpTime = 1.0
meanRepairTime = 0.5
specs = Specs { spcStartTime = 0.0,
spcStopTime = 1000.0,
spcDT = 100.0,
spcMethod = RungeKutta4,
spcGeneratorType = SimpleGenerator }
model :: Simulation LIO Double
model =
do totalUpTime <- newRef 0.0
let machine =
do upTime <-
liftParameter $
randomExponential meanUpTime
--
-- r <- liftSimulation $ newRef 10
--
holdProcess upTime
liftEvent $
modifyRef totalUpTime (+ upTime)
repairTime <-
liftParameter $
randomExponential meanRepairTime
holdProcess repairTime
machine
runProcessInStartTime machine
runProcessInStartTime machine
t2 <- liftParameter stoptime
let leaf =
do x <- readObservable totalUpTime
t <- estimateTime
let a = x / (2 * t)
return a
let reduce a1 a2 =
do let a = (a1 + a2) / 2
return a
r <- foldEstimate reduce leaf
runEstimateInStartTime r
main :: IO ()
main =
do a <- runLIO $
runSimulation model specs
print a