©GoldSim Technology Group LLC., 2012 Optimization in GoldSim Jason Lillywhite and Ryan Roper June 2012 Webinar

©GoldSim Technology Group LLC., 2012

Optimization in GoldSim

Jason Lillywhite

and

Ryan Roper

June 2012 Webinar


Agenda

Intro – Jason - 15 minutes Simple examples – Ryan – 30 minutes Submodel examples – Jason – 10 minutes Questions – 5 minutes


Why Optimization?

Finding best input values for a model Selecting best option among alternatives

– Safest, cheapest, most reliable, etc Optimizing the timing of actions


GoldSim’s Optimization Feature

Box’s complex method– Box, M. J. (1965) “A new method of

constrained optimization and comparison with other methods”

Start with initial “complex” (valid solutions) Search the solution space iteratively Replace least optimal solutions with more

optimal ones Iterate until convergence


Setting up an Optimization

Minimize/Maximize Precision Randomize optimization sequence? Define your objective function Required condition Optimization variables


Precision

Low: 2N; F < 0.01 Ri or 100 solutions Medium: 4N; F < 0.001 Ri or 1000 solutions High: 10N; F < 0.00001 Ri or 1E4 solutions Maximum: 10N; no longer improve result or 1E6 solutions

N = number of optimization variables to generate the initial complex

F = objective function

Ri = initial range


Objective Function

Define your objective function– Minimize or maximize?– Model output– Final values only!

Examples:– Cumulative cost– Total number of events– Peak value during simulation


Objective Function


Required Condition

Add another boundary to the optimization search space Examples:

– Regulatory limit– Financial budget– Restrict unacceptable combination of variables


Optimization Variables

Data or Stochastic elements Represent decision variables

– Have direct control Examples:

– Pipe size– How much to spend?– When something occurs

Objective function dependent on ALL optimization variables!


Optimization Variables


Running an Optimization

Best Function Value vs. Iterations– Plot the optimal value per iteration

Top results– Table showing objective function and

variables from the 10 most optimal iterations Interrupts are ignored during optimization runs if

continue or skip options are selected


Running the Optimization


Optimization of Complex Models

Multiple optima Choice of bounds may be important Convergence may not be possible May converge on a local optimum Randomize optimization helps search through

multiple optimal outcomes


Potential Warnings

Unable to create a valid complex– Cannot find 2N valid solutions (N=opt. vars.)

Cannot improve the solution– Found a number of valid solutions but can’t find any

better ones (stuck)– Convergence might be too strict– Examine the top results

Failure to converge– No convergence after many iterations– 100 for low, 1000 for medium, 10,000 for high

precision


Optimization of a Probabilistic Model

Objective function must be a statistic– i.e. Minimize the mean or value at 95%

Must use a submodel


Applications…

Documents

©GoldSim Technology Group LLC., 2012 Optimization in GoldSim Jason Lillywhite and Ryan Roper June 2012 Webinar