Optimization under Uncertainty in Manufacturing

If you’ve ever felt that Excel’s Solver add-in didn’t allow you to adequately model an optimization decision, then this three-part series on what Palisade can add to the optimization portion of your analytical tool kit is for you. The series will first review single-objective, deterministic optimization then go beyond this limited approach to optimization by presenting several advanced optimization concepts and demonstrating how Evolver, part of Palisade’s Decision Tools Suite, and @RISK Industrial’s RISKOptimizer can be used to solve models with these embellishments. Examples used for the demonstrations will come primarily from the manufacturing/operations management arena. By the conclusion of the three webinars, you will be able to model, solve and interpret solutions of single- and dual-objective optimization problems under deterministic conditions and conditions of uncertainty using Evolver and RISKOptimizer as appropriate. Part 2 – Optimization under Uncertainty. This webinar will present single-objective optimization under uncertainty as solved by @RISK Industrial’s RISKOptimizer. In contrast with deterministic optimization that treats all model constants as known with certainty, with RISKOptimizer model constants such as costs, profits, and resource levels can be more appropriately modeled with probability distributions, and optimization and simulation can be performed simultaneously. As a result, more robust objectives such as maximizing mean profit can be investigated. Several mostly manufacturing/operations management examples will be used to demonstrate how to model, solve and interpret the RISKOptimizer solution of this type of problem.