-- 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.Dynamics import Simulation.Aivika.Dynamics.Base import Simulation.Aivika.Dynamics.Simulation import Simulation.Aivika.Dynamics.EventQueue import Simulation.Aivika.Dynamics.Ref import Simulation.Aivika.Dynamics.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 queue <- newQueue totalUpTime <- newRef queue 0.0 pid1 <- newProcessID queue pid2 <- newProcessID queue let machine :: Process () machine = do startUpTime <- liftDynamics time upTime <- liftIO $ exprnd upRate holdProcess upTime finishUpTime <- liftDynamics time liftDynamics $ modifyRef totalUpTime (+ (finishUpTime - startUpTime)) repairTime <- liftIO $ exprnd repairRate holdProcess repairTime machine runDynamicsInStartTime $ do t0 <- starttime runProcess machine pid1 t0 runProcess machine pid2 t0 runDynamicsInStopTime $ do x <- readRef totalUpTime y <- stoptime return $ x / (2 * y) main = runSimulation model specs >>= print