Scenario tree construction driven by heuristic solutions of the optimization problem

Vit Prochazka () and Stein Wallace
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Vit Prochazka: NHH Norwegian School of Economics

Computational Management Science, 2020, vol. 17, issue 2, No 6, 277-307

Abstract: Abstract We present a new scenario generation process approach driven purely by the out-of-sample performance of a pool of solutions, obtained by some heuristic procedure. We formulate a loss function that measures the discrepancy between out-of-sample and in-sample (in-tree) performance of the solutions. By minimizing such a (usually non-linear, non-convex) loss function for a given number of scenarios, we receive an approximation of the underlying probability distribution with respect to the optimization problem. This approach is especially convenient in cases where the optimization problem is solvable only for a very limited number of scenarios, but an out-of-sample evaluation of the solution is reasonably fast. Another possible usage is the case of binary distributions, where classical scenario generation methods based on fitting the scenario tree and the underlying distribution do not work.

Keywords: Stochastic optimization; Scenario tree; Scenario generation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10287-020-00369-2

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