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Simulation

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Simulation

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  1. Simulation By Sunyoung Cho And Mark Lewis

  2. What is Simulation • Simulation – “A way to reproduce the conditions of a situation, as by means of a model, for study or testing or training, etc.” – Oxford American Dictionary (1980) • Computer simulation uses the power of a computer to carry out experimentation on a modelof the system of interest. • Applies in many fields and industries • Numerous academic fields including computer science, finance, management science, network, system dynamics, organization theory, logistics, and so on • Very popular and powerful method

  3. When to use simulation • It is often impossible to do experimentation in reality with the actual system • System doesn’t exist • Would be disruptive, expensive, or dangerous • You can use simulation in the research stages such as: • Exploration of problem and solution • Evaluation and validation

  4. Advantages • Cost – experiments on real systems may turn out to be expensive • Time – it is possible to simulate weeks, months, or even years in seconds of computer time • Replication – the real world rarely allows precise replication of an experiment, but simulations are precisely repeatable. • Safety – you can study the effect of extreme conditions, which could be dangerous, or illegal in real life.

  5. Disadvantages • Each run of a simulation only gives an estimate of true system performance. Requires statistical methods to give more precise results • Simulation models can be expensive and time-consuming to develop • Often difficult to validate the model • Large volume of output data and attractive graphics often mask problems in the inherent assumptions

  6. Different Kinds of Simulation • Static vs. Dynamic • Does time have a role in the model? • Continuous vs. Discrete • Can the “state” change continuously or only at discrete points in time? • Deterministic vs. Stochastic • Is everything for sure or is there uncertainty? • Most operational models: • Dynamic, Discrete, Stochastic

  7. What is a Model? • Model – set of assumptions/approximations about how the system works • Study the model instead of the real system … usually much easier, faster, cheaper, safer • Can try wide-ranging ideas with the model • Make your mistakes on the computer where they don’t count, rather than for real where they do count • Often, just building the model is instructive – regardless of results • Model validity (any kind of model … not just simulation) • Care in building to mimic reality faithfully • Level of detail • Get same conclusions from the model as you would from system

  8. What is a system? “A collection of elements that function together to achieve a desired goal” – (Blanchard 1991) • Key Points: • A system consists of multiple elements • Elements are interrelated and work in cooperation • Exists for the purpose of achieving specific objectives • Examples of Systems: • Traffic Systems • Political Systems • Economic Systems • Manufacturing Systems • Service Systems

  9. System Elements Incoming Entities System Outgoing Entities Activities Outcomes Resources Controls Entities - Items processed through the system such as products, customers, and documents. Activities –Tasks performed in the system that are either directly or indirectly involved in the processing of entities. Resources –The means by which activities are performed. Controls –Dictate how, when, and where activities are performed, they impose order on the system.

  10. System Variables Decision Variables – Sometimes referred to as the independent variables. Changing the value of the decision variables changes the behavior of the system. State Variables – Indicate the status of the system at any specific point in time. Response Variables – Measure the performance of the system in response to particular decision variables. Examples • Decision • # of operators to assigned to an assembly line. • State • Current number of entities waiting to be processed. • Response • Average utilization of a resource.

  11. System Complexity System complexity is a function of two factors: 1. Interdependencies 2. Variability (Interdependencies + Variability = Complexity)

  12. Phases in Simulation Building 1 2 3 • Modeling • Data collection and Analysis • Simulation model development • Validation, Verification, Calibration • Computing • Different types of models require different types of software: 1. Data driven – Suitable for less complex applications 2. Bespoke Program – In cases with many entity classes each with many members • Experimentation • Experiment must be planned so that the various factors which may influence the results can be disentangled. • Experimenter must consider how long to run the simulation • Experimenter must be familiar with the appropriate statistical methods.

  13. Important Steps in Building a Simulation 1. Define an achievable goal 2. Create a diverse team with different skills Skills Needed: Knowledge of system, model building, data collection, statistical (input/output data representation), managerial. 3. Model the appropriate level(s) of detail Define boundaries of the system, some characteristics of the environment may need to be included, control tendency to model in great detail well understood parts of the system and neglecting less known parts.

  14. Important Steps in Building a Simulation Model (continued) 4. Develop a plan for model verification 5. Develop a plan for model validation Insure the model represents the system under investigation 6. Develop a plan for statistical output analysis

  15. Journals • ACM Transactions on Modelling and Computer Simulation • Computer Simulation Modeling and Analysis • European Journal of Operations Research • IEEE Journal of Systems, Man and Cybernetics • IIE Transactions on IE Research • International Journal in Computer Simulation • Management Science • ORSA Journal on Computing • Simulation • System Dynamics Review • Journal of the Operational Research Society

  16. References • Averill M. Law, W. David, Kelton,2000 “Simulation Modeling and Analysis”, McGraw-Hill • Charles Harrell, et al., 2000, “Simulation Using ProModel”, McGraw-Hill • Ramsey Suliman, et al.,2000 “Tools and Techniques for Social Science Simulation”, Physica Verlag • Michael Pidd, 1998, “Computer Simulation in Management Science”, John Wiley & Sons • Michael Prietula, et al., 1998, “Simulating Organizations: Computational Models of Institutions and Groups”, Mit. Press • David Profozich,1997, “Managing Change with Business Process Simulation”, Pearson Ptr. • Paul A. Fishwick, Richard B. Modjeski, 1991, “Knowledge-Based Simulation”,Springer-Verlag • Klaus G. Troitzsch, et al., 1996, “Social Science Microsimulation”, Springer Verlag • Harry A. Pappo, 1998, “Simulations for Skills Training”, Educational Technology Publications