Exploring algorithmic fairness in robust graph covering problems
Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings
subject to unanticipated challenges with complex social effects. Motivated by real-world …
subject to unanticipated challenges with complex social effects. Motivated by real-world …
Decomposition-based approaches for a class of two-stage robust binary optimization problems
AN Arslan, B Detienne - INFORMS journal on computing, 2022 - pubsonline.informs.org
In this paper, we study a class of two-stage robust binary optimization problems with
objective uncertainty, where recourse decisions are restricted to be mixed-binary. For these …
objective uncertainty, where recourse decisions are restricted to be mixed-binary. For these …
Robust active preference elicitation
We study the problem of eliciting the preferences of a decision-maker through a moderate
number of pairwise comparison queries to make them a high quality recommendation for a …
number of pairwise comparison queries to make them a high quality recommendation for a …
Robust optimization with decision-dependent information discovery
Robust optimization is a popular paradigm for modeling and solving two-and multi-stage
decision-making problems affected by uncertainty. In many real-world applications, the time …
decision-making problems affected by uncertainty. In many real-world applications, the time …
A Double-oracle, Logic-based Benders decomposition approach to solve the K-adaptability problem
The K-adaptability problem is a special case of adaptive robust optimization with discrete
recourse that aims to prepare K solutions under uncertainty, and select among them upon …
recourse that aims to prepare K solutions under uncertainty, and select among them upon …
Min–max–min robustness for combinatorial problems with discrete budgeted uncertainty
We consider robust combinatorial optimization problems with cost uncertainty where the
decision maker can prepare K solutions beforehand and chooses the best of them once the …
decision maker can prepare K solutions beforehand and chooses the best of them once the …
On the complexity of min–max–min robustness with two alternatives and budgeted uncertainty
A Chassein, M Goerigk - Discrete Applied Mathematics, 2021 - Elsevier
We study robust solutions for combinatorial optimization problems with budgeted uncertainty
sets in the min–max–min setting, where the decision maker is allowed to choose a set of k …
sets in the min–max–min setting, where the decision maker is allowed to choose a set of k …
Exact and Approximate Schemes for Robust Optimization Problems with Decision-Dependent Information Discovery
Uncertain optimization problems with decision-dependent information discovery allow the
decision maker to control the timing of information discovery, in contrast to the classic …
decision maker to control the timing of information discovery, in contrast to the classic …
Min-Max-Min Optimization with Smooth and Strongly Convex Objectives
We consider min-max-min optimization with smooth and strongly convex objectives. Our
motivation for studying this class of problems stems from its connection to the-center …
motivation for studying this class of problems stems from its connection to the-center …
[PDF][PDF] New complexity results and algorithms for min-max-min robust combinatorial optimization
J Kurtz - arxiv preprint arxiv:2106.03107, 2021 - optimization-online.org
In this work we investigate the min-max-min robust optimization problem applied to
combinatorial problems with uncertain cost-vectors which are contained in a convex …
combinatorial problems with uncertain cost-vectors which are contained in a convex …