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Course Description

In linear models, the value of the objective function changes smoothly as any given decision value changes. This predictability is what allows certainty that the optimal solution is, without question, the best result given the model's structure, constraints, and fixed parameters. But often the world is not predictable. Likewise, the decisions we need to make often do not have values that lie along a continuum. They are often restricted to integer values (...-1, 0, 1, 2, 3, etc.) or are yes/no or go/no-go decisions. In this lesson, you will see how to extend an optimization model using binary decision variables to build in a new constraint. This incremental step in complexity reflects the nature of the optimization modeling process. To the extent that you can make modest, low-cost improvements to a model, you will continue to do so, provided your model continues to produce usable results. By completing this lesson, you will hopefully continue to explore what is possible with optimization and what techniques and tools might serve you well.

Benefits to the Learner

  • Extend an existing optimization model using binary decision variables to build in a new constraint
  • Use summary statistics to approximate optimal results for a stochastic problem
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Type
self-paced (non-instructor led)
Dates
Mar 05, 2019 to Dec 31, 2030
Total Number of Hours
1.0
Course Fee(s)
Regular Price $0.00
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