Discrete event simulation Prof.dr.ir. Alexander Verbraeck - - PowerPoint PPT Presentation
Discrete event simulation Prof.dr.ir. Alexander Verbraeck Professor, Faculty of TPM, TU Delft Overview What is discrete event simulation? Where does it fit historically? How does it differ from other types of simulation?
Prof.dr.ir. Alexander Verbraeck
Professor, Faculty of TPM, TU Delft
simulation?
study?
simulation for infrastructure studies?
Instrument to:
Mainly used for logistical problems:
resources In some cases more advanced use:
mathematics engineering/electronics control theory biology world war II
cybernetics general systems theory 1930 systems analysis policy analysis 1950 1960 1970 system dynamics soft systems methodology complex systems cas 1980 1990 systems engineering engineering
Slide courtesy Els van Daalen, TU Delft
mathematics engineering/electronics control theory biology world war II
cybernetics general systems theory 1930 systems analysis policy analysis 1950 1960 1970 system dynamics soft systems methodology complex systems cas 1980 1990 systems engineering engineering
Slide courtesy Els van Daalen, TU Delft
discrete event simulation SD modeling agent based modeling
relationships between variables
evolution of variables over time
at a given time is called the state of the model
models, state changes occur at an instant of time
state, occurring at an instant
Nance, 1981
In continuous models, state is a continuous function of time:
20 40 60 80 100 120 140 160 1 11 21 31 41 51 61 71 81 91
In discrete-event models, state is a piecewise constant function over time:
20 40 60 80 100 120 140 160 10 20 30 40 50 60 70 80 90 100
Very useful for:
For all these systems it means that we have to focus on the events, i.e. the start and the end of processes rather than the evolution of the process itself
model “as is” models “to be” current situation new situation
search for solutions pre- evaluation diagnose problem validation problem identification and specification choice and implementation post- evaluation evaluation
Traditional: waterfall model
But better: incremental modeling
Conceptua- lisation Specification Data- collection Verification/ Validation ...
Traditional: waterfall model
But better: incremental modeling
Conceptua- lisation Specification Data- collection Verification/ validation Treatment ...
Traditional: waterfall model
But better: incremental modeling
Conceptua- lisation Specifi- cation Verification / Validation Experimentation Analysis Diagnosis Data- collection Treatment
Start small...
Output: a number of conceptual models that can be used to describe the system
described:
Object model
ActiveInfra SingleInfra CompoundInfra Storage Station LoadingStation UnloadingStation Straight Curve TwoWayBranch TwoWayJunction EntryStation Helix EarlyBagageBelt Caroussel ControlStation Lateral CV_TTUnloadingStation CV_DVCUnloadingStation CheckIN
Process model
Output: working model that can be experimented with
Output: simulation model that is correct and is a good representation of the real system
conceptual model)
model
system values
Sargent, R.G. (2009). VERIFICATION AND VALIDATION OF SIMULATION MODELS. In: M. D. Rossetti, et al. (Eds.) Proceedings of the 2009 Winter Simulation Conference, IEEE, 2009, pp. 162 - 176.
Output: the run control conditions under which the system, or the model of it, is experimented with or observed
Output: results of analysis and diagnosis of the experiments with the model of the current situation
resources, etc.)
Statistical analysis
as simulation model components
"bottom-up"
in multiple simulation languages
↔ resource capacity and usage
debugging and presenting
components that gather many different statistics