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Optimization
Vensim's optimizer provides fast calibration
of models and discovery of optimal solutions
Model Calibration
Validation of the integrity of a model rests in part on comparing model
behavior to time series data collected in the "real world." When
a model is structurally complete and simulates properly, calibration of
the model can proceed to fit the model to this observed data. Dynamic
models are often very sensitive to the values of constant parameters.
If you want to calibrate your parameters so the model behavior matches
observed data, you may need to experiment with thousands of combinations
of different parameter values. Vensim calibration makes this procedure
automatic. You specify which data series you want to fit and which
parameters you want to adjust, then Vensim automatically adjust parameters
to get the best match between model behavior and the data. There
are no limits on the numbers of parameters to adjust or data series to
fit.
Example
Over the last century, the conversion of U.S. households to electricity
follows a pattern of diffusion, represented by a Bass Diffusion model.
Once the structure is complete and produces the desired behavior patterns,
we can calibrate the model to fit historical data from the U.S. Department
of Commerce. The first simulation run (first run) shows growth too
early and too fast compared to the historical data (historical).
The user selects the parameters that have unknown or uncertain values and
selects the data series historical, then the Vensim optimization engine
searches for the values of the parameters that gives the best fit to the
data, and displays the values of the parameters and the best simulation
run (calibrated).
Policy Optimization
Vensim's optimizing engine can search through a large space of parameter
values looking for optimal solutions. You define the payoff variables
you want to adjust. An efficient Powell hill climbing algorithm searches
through the parameter space looking for the largest cumulative payoff.
There are no limits on the numbers of payoff variables or policy parameters
to search over. Advanced sensitivity analysis is available from optimization
simulations.
Example
A company features an inventory and sales supply chain which it must manage
to maximize company profits. Too little inventory leads to loss of
sales (and lower profits) but too much inventory costs more to stock and
also leads to lower profits. The user selects the payoff variables,
in this case Cumulative Profit, and then selects the policy parameters
(minimum inventory value to start restocking, and maximum inventory value
to stop restocking). The Vensim optimization engine searches for
the values of the policy parameters that gives the best payoff value (highest
Cumulative Profit) and prints out the values and the optimal simulation
run.
Kalman Filtering
In a dynamic system with unobserved variables it is desirable, but impossible
to know, the state of all variables at all times. However, if values
for some of the variables are known, you can make a good estimate of the
values of other variables. For example, a business model might
consist of a Workforce/Inventory system. The goal is to determine
the time profiles of workforce, inventory, and other model variables given
the available measurements. Vensim simulates the model with the Kalman
filter active and makes intelligent choices for the output, based on both
the model Simple Simulation data (simple.vdf in graph below) and Measured
Inventory data (data.vdf). The resulting output Kalman Filtering (filter.vdf - grey line) tracks the Actual Inventory (blue line) much better
than either the simple simulation alone (red line) or the measured inventory
alone (green line).
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