Title: The Value of an Approximate Solution
in Stochastic Programming with Recourse
Derek Holmes
Abstract of Recent Presentation:
Explicitly considering uncertainty in mathematical programming involves
several modeling decisions. Due to the difficulty of solving stochastic
programs, simplifications are frequently used. Measuring the costs of
making these simplifications gives modelers quantitative tools for
guiding and evaluating the modeling process. For example, the Expected
Value of Perfect Information (EVPI) measures the relative benefits of
eliminating uncertainty. We use a taxonomy of such measures to define
the Value of an Approximate Solution. The VAS is the (potential) cost
of using a suboptimal decision in terms of bounding functions on the true
optimal cost. An algorithmic approach to bounding the VAS is presented.
Primal and dual solution techniques will be presented, as well as
refinement procedures.
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