Distributionally robust optimization: A review
The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …
have developed significantly over the last decade. Statistical learning community has also …
Frameworks and results in distributionally robust optimization
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
Wasserstein distributionally robust optimization: Theory and applications in machine learning
Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …
parameters whose distribution is only indirectly observable through samples. The goal of …
Optimization-based scenario reduction for data-driven two-stage stochastic optimization
We propose a novel, optimization-based method that takes into account the objective and
problem structure for reducing the number of scenarios, m, needed for solving two-stage …
problem structure for reducing the number of scenarios, m, needed for solving two-stage …
Problem-driven scenario clustering in stochastic optimization
In stochastic optimisation, the large number of scenarios required to faithfully represent the
underlying uncertainty is often a barrier to finding efficient numerical solutions. This …
underlying uncertainty is often a barrier to finding efficient numerical solutions. This …
Low budget active learning via wasserstein distance: An integer programming approach
Active learning is the process of training a model with limited labeled data by selecting a
core subset of an unlabeled data pool to label. The large scale of data sets used in deep …
core subset of an unlabeled data pool to label. The large scale of data sets used in deep …
Optimizing the inventory and fulfillment of an omnichannel retailer: a stochastic approach with scenario clustering
We study an inventory optimization problem for a retailer that faces stochastic online and in-
store demand in a selling season of fixed length. The retailer has to decide the initial …
store demand in a selling season of fixed length. The retailer has to decide the initial …
Optimal scenario reduction for one-and two-stage robust optimization with discrete uncertainty in the objective
Robust optimization typically follows a worst-case perspective, where a single scenario may
determine the objective value of a given solution. Accordingly, it is a challenging task to …
determine the objective value of a given solution. Accordingly, it is a challenging task to …
Semi-discrete optimal transport: Hardness, regularization and numerical solution
Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between
a discrete and a generic (possibly non-discrete) probability measure, are believed to be …
a discrete and a generic (possibly non-discrete) probability measure, are believed to be …
Scenario generation by selection from historical data
M Kaut - Computational Management Science, 2021 - Springer
In this paper, we present and compare several methods for generating scenarios for
stochastic-programming models by direct selection from historical data. The methods range …
stochastic-programming models by direct selection from historical data. The methods range …