社会系统仿真ppt课件.ppt

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1、12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,1,I. Introduction,M. Peter JurkatCS452/Mgt532 Simulation for Managerial DecisionsThe Robert O. Anderson Schools of ManagementUniversity of New Mexico,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,2,Definitions,Simulation: process of experimenting wit

2、h a model of a dynamic systems (e.g., process) to study or test the behavior of the system improve, problem solvedesign and/or select new systems , and/ortrain operators on a model of an existing systemsSystem: purposeful, interrelated components with interdependencies and complexityBehavior: purpos

3、eful, interrelated sequences of activitiesDynamic: time varying (static systems are dull!),12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,3,Examples,Service Systems:Traffic on Networks: messages to/from computers, cars on roads/rails, airplanes to/from airports/gates, ships to/from harbors/piers, e

4、levatorsRetail/Service :stores selling goods, service/repair shops, logistics/inventory/distribution/MRPManufacturing Systems:Materials, Chemicals, BiologicalsAppliances, Automobiles/Trucks, Toys, ClothingElectronics, Weapons SystemsComputations using models from other disciplinesMacroeconomic: taxa

5、tion/interest rate cost/benefitsPollution: environmental intervention cost/benefitsProject Management: completion time vs resources,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,4,Why Simulate?,To overcome human limitations inPhysical capability: avoid injury and death; be able to control systems

6、whose dynamics are not yet known, Mental capability: attention, memory, processing, Analysis: allows us to study systems too complex for analytic description and/or too dangerous for human safety gain knowledgeDesign: attempt changes in IVs to drive one or more DVs toward an “optimal” value or combi

7、nation of values for design, improvement, and/or problem solving,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,5,When not to Simulate!,When theory can determine sufficient resultsWhen it will cost more to simulate than the return on the knowledge gainedWhen there is incomplete information about th

8、e system (can handle imprecise but not missing pieces)Need at least inputs and related outputs for “black boxes”Can assume missing information and check against known results if agreement, support for assumptionsWhen it is not possible to develop a representative, tractable simplification of the sys

9、tem,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,6,Definitions (cont.),Model: representation of a system three phases:Verbal always included in any representationGraphical see pages 22, 39, 50, 54, 367, and 536 Algorithm and/or computer programExperimentation: purposeful, structured, and controll

10、ed change of the inputs factors (independent variables IVs, exogenous, ) of a product and/or process to observe resulting changes in outputs (dependent variables - DVs, responses, results, outcomes, )Both IVs, DVs also called measures or metricsIn simulation literature a run is one execution of the

11、simulation program at one combination of input variable values also called a replication,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,7,Graphical Representation:Logical Symbols,BCNN 4th Ed., Figure 2.1, page 22: Single Server Queuing System,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,8,Graphi

12、cal Representation:State Variable Tracking,BCNN 4th Ed., Example 2.2, Figure 2.11, page 39,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,9,Graphical Representation:Physical Layout,BCNN 4th Ed., Example 2.6, Figure 2.15, page 50,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,10,Graphical Represent

13、ation:Network Model,BCNN 4th Ed., Example 2.8, Figure 2.18, page 54,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,11,Graphical Representation:“Black Box”,BCNN 4th Ed., Figure 10.5, page 367,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,12,Graphical Representation:Component Relationship,BCNN 4th

14、Ed., Example 14.4, Figure 14.10, page536: Website configuration,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,13,Simulation Study Representation(after Banks et al, Figure 1.3, Page 15),Problem Formulation,Set Objectives and Project Plan,(Re)Conceptualize Model & Collect Data,Translate Model,Can Mo

15、del be Verified?,No,Can Model be Validated?,Yes,No,DOE - Design Experiments,Runs and Replications,Analysis,Results Clear and Able to be Described?,No,Document, Report and Recommend,Yes,Yes,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,14,Simulation Study,Identify problem(s), improvement(s), and/or

16、 plan new capabilitiesSpecify the system select boundaries, identify inputs, entities, attributes, events, activities, processes, and state variables - specify output(s) and their desired valuesBuild a conceptual and operational model of the system build a representation of inputs, entities, ,12/3/2

17、022,MPJ/UNM CS452/Mgt 532 I. Introduction,15,Simulation Study (cont.),Verify and Validate (as best you can) the operational model against existing system only partial model verification/validation may be possible for new systemsPerform screening experiment(s) to identify IVs with significant effect

18、on desired output(s) proceed with only these IVsSelect ranges of IVs which reduce variability to acceptable levels, if necessary (Critical Step!)Experiment with model to identify values of inputs which “optimize” output or “achieve” goalBuild system or prototype to test results of study,12/3/2022,MP

19、J/UNM CS452/Mgt 532 I. Introduction,16,System Description, Problem, Objectives, Project Plan,Verbal description/linguistic analysisIdentify problems and/or (re)design objectivesIdentifying relevantEntitiesAttributesEventsActivities/processes, andstate variablesto address problem(s) and/or objectives

20、Develop project plan may follow STEPS FOR EXPERIMENTAL DESIGN in Schmidt and Launsby on pages I-26 and I-27,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,17,Simulation Model Components,Entities: named physical/conceptual objects (improper nouns used for UML classes, proper nouns for UML objects)At

21、tributes: named characteristic or property (adjectives)Methods: named activities or operations the entity can perform (predicates = verb + direct/indirect object(s)States: named set of conditions, standings, circumstances, and positions describing an entity at a particular time (adjectives, verbal n

22、ouns = gerunds)Processes: named groups of activitiesEvents: named noteworthy occurrences, often at the beginning or completion of one or more activities and/or processes,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,18,Identify Variables,Output (dependent) variables whose values will be the proble

23、m solution/design improvementOperational definitionsRange of valuesAppropriate output analysisTransient vs. steady stateStatistical tools (confidence intervals, t-tests, ANOVA, regression/model building),12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,19,Identify Variables (cont.),Factors among whos

24、e combination of values will provide the problem solution of optimum designThese will be varied by the investigator according to some experimental design (DOE)Operational definitions, range of values, level values, potential interactions (for eventual assignment to DOE columns)Factor model: relates

25、factors to output variables developed in modeling experiments,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,20,Identify Variables (cont.),State variables whose change of values determine the eventsOther variables necessary for a complete modelIdentify stochastic variables and collect data to speci

26、fy their distributionsIf close to known mathematical distributions then identify their parametersElse use as empirical distributionsCollect data for constants these may have to be fitted from the data,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,21,(Re)Conceptualize Simulation Model and Collect D

27、ata,Simulation model: relates all variables to output variablesRepresentation tools:natural or domain specific language/jargonmathematical notationcode (e.g., Java, GPSS) and pseudo-code (primitive action, choice, iteration)flow chartsUMLPERT/CPM diagramspictorial imagesstoryboards/moviesBuild Simul

28、ation Model and the Simulation itself,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,22,Verify and Validate,Verify that calculations in implementation are correctValidate the results against output known to be an accurate reflection of realityMay only be possible for parts of the model or highly re

29、stricted situationsIf not make “reasonableness checks”,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,23,Design and Conduct Experimental Runs,Do experimentsScreen: experimental runs (2-level?) to find the significant few factorsModel: further or new set of experimental runs (3 or 5 levels) to devel

30、op factor model equationsfit equations by regressionOptimize:solve equations for optimum ormake experimental runs to drill down to best combinations of factorsCheck: local optimum (simulate all neighbors),12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,24,Solutions/Design Identification and Report,F

31、rom simulation runs identify the solution to the problem and/or the optimum designWrite ReportAbstract (may only be needed for research or archive reports)Executive Summary: non-technical problem statement, solution/design, justification (not usually in research reports)Technical Report: complete de

32、tails so that entire project could be repeated by others including equations, code, distributions, run resultsTechnical Appendix,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,25,Simulation ReportSee SimulationStudy&ReportOutline.doc for details of each section,Abstract Executive SummaryFull Techni

33、cal ReportSituation, Problems, Opportunities, Goals, and ObjectivesBackgroundSystem SpecificationPerformance MeasuresInput FactorsSystem Representation/ModelProject ActivitiesInput Specification and Model ImplementationVerification and ValidationExperiments and Results of the Simulation RunsAnalysis

34、 and ResultsConclusion and RecommendationsTechnical Appendix,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,26,Assignments,Choose one application from Banks 1.1 or your selection for a DESS project: write sections 3.a)-c) of the report (specify the entities & make a symbolic representation using fl

35、ow charts, UML, or ). This can be a group exercise.Individual exercises, Banks 1.6Prepare a brief written report (include copy of papers if possible) andPrepare an even briefer set of slides for presentation to the class (unless the subject of your paper is particularly interesting you may not be as

36、ked to actually make the presentation in any case the presentation will be informal),12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,27,Model Classification,Does system evolve over time?Static: one time period or steady stateDynamic: changes occur over time period of interestHow often do we have to

37、specify changes?Discrete Event: changes only occur at instances separated in timeContinuous Event: changes occur constantlyHow predictable is the system?Deterministic: we assume we can model the system as if we know all that needs to be known about the systemStochastic (Stochs): we know certain aspects of the system only as a probability distributionTotally Unpredictable: cannot model,12/3/2022,MPJ/UNM CS452/Mgt 532 I. Introduction,28,How Various Models are Studied,