K-adaptability in stochastic combinatorial optimization under objective uncertainty

Christoph Buchheim and Jonas Pruente

European Journal of Operational Research, 2019, vol. 277, issue 3, 953-963

Abstract: We address combinatorial optimization problems with uncertain objective functions, given by discrete probability distributions. Within this setting, we investigate the so-called K-adaptability approach: the aim is to calculate a set of K feasible solutions such that the objective value of the best of these solutions, calculated in each scenario independently, is optimal in expectation. Interpreted as a stochastic optimization problem, we only consider second-stage variables, however, the corresponding candidate solutions are selected in the first stage, i.e., before the scenario is known.

Keywords: Stochastic programming; K-adaptability; Integer programming; Approximation; Parameterized complexity (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:277:y:2019:i:3:p:953-963

DOI: 10.1016/j.ejor.2019.03.045

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