-- 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 System.Random import Control.Monad.Trans import Simulation.Aivika.Specs import Simulation.Aivika.Simulation import Simulation.Aivika.Event import Simulation.Aivika.Dynamics import Simulation.Aivika.Ref import Simulation.Aivika.Process upRate = 1.0 / 1.0 -- reciprocal of mean up time repairRate = 1.0 / 0.5 -- reciprocal of mean repair time specs = Specs { spcStartTime = 0.0, spcStopTime = 1000.0, spcDT = 1.0, spcMethod = RungeKutta4 } exprnd :: Double -> IO Double exprnd lambda = do x <- getStdRandom random return (- log x / lambda) model :: Simulation Double model = do totalUpTime <- newRef 0.0 pid1 <- newProcessId pid2 <- newProcessId let machine :: Process () machine = do startUpTime <- liftDynamics time upTime <- liftIO $ exprnd upRate holdProcess upTime finishUpTime <- liftDynamics time liftEvent $ modifyRef totalUpTime (+ (finishUpTime - startUpTime)) repairTime <- liftIO $ exprnd repairRate holdProcess repairTime machine runProcessInStartTime IncludingCurrentEvents pid1 machine runProcessInStartTime IncludingCurrentEvents pid2 machine runEventInStopTime IncludingCurrentEvents $ do x <- readRef totalUpTime y <- liftDynamics stoptime return $ x / (2 * y) main = runSimulation model specs >>= print