-- 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.Trans import Simulation.Aivika meanUpTime = 1.0 meanRepairTime = 0.5 specs = Specs { spcStartTime = 0.0, spcStopTime = 1000.0, spcDT = 1.0, spcMethod = RungeKutta4, spcGeneratorType = SimpleGenerator } model :: Simulation Results model = do totalUpTime <- newRef 0.0 let machineBroken :: Double -> Event () machineBroken startUpTime = do finishUpTime <- liftDynamics time modifyRef totalUpTime (+ (finishUpTime - startUpTime)) repairTime <- liftParameter $ randomExponential meanRepairTime -- enqueue a new event let t = finishUpTime + repairTime enqueueEvent t machineRepaired machineRepaired :: Event () machineRepaired = do startUpTime <- liftDynamics time upTime <- liftParameter $ randomExponential meanUpTime -- enqueue a new event let t = startUpTime + upTime enqueueEvent t $ machineBroken startUpTime runEventInStartTime $ do -- start the first machine machineRepaired -- start the second machine machineRepaired let upTimeProp = do x <- readRef totalUpTime y <- liftDynamics time return $ x / (2 * y) return $ results [resultSource "upTimeProp" "The long-run proportion of up time (~ 0.66)" upTimeProp] main = printSimulationResultsInStopTime printResultSourceInEnglish model specs