Robert M. Saltzman © DS 851: 4 Main Components 1.Applications The more you see, the better 2.Probability & Statistics Computer does most of the work 3.A Modeling Environment Arena 4.Simulation Methodology Issues faced by any simulation modeler

Robert M. Saltzman © Applications Imitation is a good way to learn Focus on business & public sector problems EXAMPLES: Call Centers: How many agents are needed? Traffic & Parking: How does behavior affect availability? Airports: Can delays & missed flights be minimized? Ambulance Deployment: What’s the best dispatch rule? Financial Planning: What’s the effect of uncertainty?  Article list: we’ll hear about many applications

Robert M. Saltzman © Probability & Statistics: Why? INPUT Most systems contain uncertainty (randomness) Model inputs include probability distributions EX: Service time – how to represent in the model? OUTPUT Simulation models generate variable output Each model run just gives 1 approximate answer EX: Customer flow time – what’s a 95% CI for  ?

Robert M. Saltzman © Modeling Environment: Arena Professional-quality package at low cost –Lots of preprogrammed features Graphical interface; minimal coding Easy to animate Integrated environment: –Input Analyzer; Debugger; Output Analysis Supported by one of the market leaders

Robert M. Saltzman © Arena - Drawbacks Professional => Non-trivial package –There are a lots of things to learn Some academic version limitations –Can only have 150 entities in system at once –Can only have 100 modules in model Professional version is quite expensive

Robert M. Saltzman © Methodology Issues Scattered throughout the course EXAMPLES: How big of a model should I build? How much input data should I collect? How do I analyze the output? How do I validate my model? What factors affect implementation?

Robert M. Saltzman © Main Types of Simulations I. PHYSICAL SF Bay & Delta Model (Sausalito) Shoe tester (Exploratorium, SF) Wind tunnels (NASA Ames, Mtn. View) Weightlessness training (NASA, Houston) Car crash dummies (Mercedes Benz)

Robert M. Saltzman © Types of Simulations - continued II. COMPUTER-BASED: Substitute a computer model for real system, and observe its behavior. Deterministic: No uncertainty. –Describe key relationships with equations –Try a set of parameter values & calculate –Recalculate for various sets of parameter values Probabilistic: Randomness is involved. –Static (“Monte Carlo”): Repeated trials with sampling –Dynamic: System evolves over time

Robert M. Saltzman © Why use models, in general? Convenience –e.g., use a map to locate places in the world Experimentation & Sensitivity Analysis Test alternative policies to make a decision: –Probably cheaper to use model –Maybe faster, safer Organize your knowledge about the system Real system may not yet exist

Robert M. Saltzman © Why use simulation models? To estimate … average system behavior; variation; effect of adding resources … To compare … alternative system designs; various operating policies, scheduling rules … To visualize … almost any process, even very complex ones. To educate … get others involved, enthused.

Robert M. Saltzman © What can’t simulation models do? Can’t give meaningful results if input data are inaccurate (GIGO) Can’t reflect structures that are not modeled Can’t give exact solutions Can’t directly tell you the optimal strategy, though it may be inferred by experimenting How does simulation compare to LP?

Robert M. Saltzman © When to use simulation For analyzing systems with –several sources of randomness, or –complicated structure, or –unusual operating rules, or –nonlinear relationships, or –entities that interact with one another When other analytical frameworks are too limiting, e.g., queuing model assumptions Simulation models have few limitations, but they may take longer to develop