Greenwicher/PyPRS
The python implementation of Partition-based Random Search for stochastic multi-objective optimization via simulation
This project helps operations engineers, product managers, or business analysts find the best trade-offs when optimizing a system where outcomes are uncertain and evaluated through simulations. You provide a simulation model and define the objectives (e.g., maximize profit, minimize cost, maximize customer satisfaction). The tool then efficiently explores the possibilities to identify a set of optimal solutions, showing you the range of best options rather than a single 'perfect' answer.
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Use this if you need to optimize a system with multiple conflicting objectives (like cost and performance) where evaluating changes requires running a simulation and the results might be noisy or variable.
Not ideal if your optimization problem has a clear, deterministic mathematical formula and doesn't involve noisy simulation outputs or discrete decision spaces.
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Last pushed
Oct 04, 2017
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