Bài giảng Operations Management - Module F: Simulation

Operations ManagementSimulationModule F1OutlineWhat is Simulation?Advantages and Disadvantages of SimulationMonte Carlo SimulationSimulation of a Queuing ProblemSimulation and Inventory AnalysisThe Role of Computers in Simulation2Learning ObjectivesWhen you complete this chapter, you should be able to Identify or Define:Monte Carlo simulationRandom numbersRandom number intervalSimulation software Explain or be able to use:The advantages and disadvantages of modeling with simulationThe use of Excel spreadsheets in simulation3Numerical technique of experimentationAttempts to duplicate a systemFeaturesBehaviorRequires description of systemMany application areasOperations managementFinance & economicsSimulation4Some Applications of SimulationAmbulance location and dispatchingBus schedulingAssembly-line balancingDesign of library operationsParking lot and harbor designTaxi, truck, and railroad dispatchingDistribution system designProduction facility schedulingScheduling aircraftPlant layoutLabor-hiring decisionsCapital investmentsPersonnel schedulingProduction schedulingTraffic-light timingSales forecastingVoting pattern predictionInventory planning and control5SimulationThe idea behind simulation is to:Imitate a real-world situation mathematicallyStudy its properties and operating characteristicsDraw conclusions and make action recommendations based on the results of the simulation6The Process of SimulationDefine the ProblemIntroduce important variablesConstruct simulation modelSpecify values of variables to be testedConduct the simulationExamine the resultsSelect best course of action7Advantages of SimulationSimulationflexible, straightforwardcan analyze large, complex real-world problems for which no closed-form analytical solutions existscan include real-world complications which most other techniques cannotenables “time compression”allows “what if” type questionsdoes not interfere with the real-world systemallows study of relationships8Simulation:Can be expensive and time consumingDoes not yield optimal solutionRequires good managerial inputResults not generalizable to other situations© 1984-1994 T/Maker Co.Disadvantages of Simulation9The Monte Carlo Simulation TechniqueSetup probability distribution for important variablesBuild cumulative distribution for each variableEstablish interval of random numbers for each variableGenerate random numbersSimulate a series of trials10Partial Table of Random Numbers(upper left corner)5206508853301047993766913537632802743524032960748590825768280594031127799087926902364971993210752195909498949036067823678985292125965262874956492378717290573369272111609589684817893450335095134434626339552930883218506257345662311540909030362460825174303536850150486118852308541712806924278821626964483112730268004514463213496662744186989211Real World Variables Which Are Probabilistic in NatureInventory demandLead time for orders to arriveTime between machine breakdownsTimes between arrivals at a service facilityService timesTimes to complete project activitiesNumber of employees absent from work each day12Simulation and Inventory Analysis - the Basic ModelBeginIncreasecurrent invby qty orderend inv =begin-demand# of lost salesEnd inv = 0Generate Random lead timePlaceorderCompute averagesEnough Days in simulation?Order placed& not arrived?End inv begin inv?Orderarrived?random #for today'sdemand13Simulation – An ExampleFollowing long trips down the Mississippi River from industrial mid-western cities, fully loaded barges arrive in New Orleans. The inter-arrival times for the barges are given in Dist. 1. In the same table, the cumulative probabilities and corresponding random number intervals are also given. Dist. 2. provides similar information regarding the times taken to unload a barge.14Example: Dist. 1 – Inter-Arrival TimesTime Between Arrivals (Hours)ProbabilityCumulative ProbabilityRandom -Number Interval360.130.1301 – 13240.170.3014 – 30120.150.4531 – 4580.250.7045 – 7060.200.9071 - 9040.101.0091 - 0015Example: Dist. 2 – Unloading TimesUnloading Times (Hours)ProbabilityCumulative ProbabilityRandom-Number Interval240.050.0501 – 05120.150.2006 – 2080.500.7021 – 7060.200.9071 – 9040.101.0091 - 0016Example: SimulatingRnInt Arr TimeArrival TimeUnloading StartsRnUnloading TimeUnloading EndsWaiting Time5288837816006364444638520502468682887608867476022498253882987461041630241061063581140103614214224815004781501500324174099415417429818220From Dist. 1From Dist. 2From Random Number Table17Example: Some Simple StatisticsAverage Time Between Arrivals (Hours)Average Time to Unload (Hours)Total Wait Time (Hours)Average Wait Time (Hours)Average Time in Port154/9 hrs102/9 hrs38 hrs38/9 hrs11.3 + 4.2 hrs17.1 hrs11.3 hrs4.2 hrs15.5 hrs18