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

©GoldSim Technology Group LLC., 2012 Agenda Intro – Jason - 15 minutes Simple examples – Ryan – 30 minutes Submodel examples – Jason – 10 minutes Questions – 5 minutes

©GoldSim Technology Group LLC., 2012 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 Technology Group LLC., 2012 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

©GoldSim Technology Group LLC., 2012 Setting up an Optimization Minimize/Maximize Precision Randomize optimization sequence? Define your objective function Required condition Optimization variables

©GoldSim Technology Group LLC., 2012 Precision Low: 2N; F < 0.01 Ri or 100 solutions Medium: 4N; F < Ri or 1000 solutions High: 10N; F < 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

©GoldSim Technology Group LLC., 2012 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

©GoldSim Technology Group LLC., 2012 Objective Function

©GoldSim Technology Group LLC., 2012 Required Condition Add another boundary to the optimization search space Examples: –Regulatory limit –Financial budget –Restrict unacceptable combination of variables

©GoldSim Technology Group LLC., 2012 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!

©GoldSim Technology Group LLC., 2012 Optimization Variables

©GoldSim Technology Group LLC., 2012 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

©GoldSim Technology Group LLC., 2012 Running the Optimization

©GoldSim Technology Group LLC., 2012 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

©GoldSim Technology Group LLC., 2012 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

©GoldSim Technology Group LLC., 2012 Optimization of a Probabilistic Model Objective function must be a statistic –i.e. Minimize the mean or value at 95% Must use a submodel

©GoldSim Technology Group LLC., 2012 Applications…