XVII th Conference on Stochastic Programming

Session

(Distributionally) Robust Optimization

(D)RO

28 juil. 2025, 16:30

F107

Présidents de session

(Distributionally) Robust Optimization: Exploring Structural Perspectives in Distributionally Robust Optimization

• Yiling Zhang

(Distributionally) Robust Optimization: Robust Inference and Learning: Advances in Theory and Applications

• Angelos Georghiou (University of Cyprus)

(Distributionally) Robust Optimization: Advancing Robust/Data-Driven Optimization Strategies for Resilience and Social Impact

• Cagil Kocyigit (University of Luxembourg)

(Distributionally) Robust Optimization: Contributed talks

• Eojin Han (University of Notre Dame)

(Distributionally) Robust Optimization: Contributed talks

• Renjie Chen

276. Intuitive and Fast Algorithms for Two-Stage Distributionally Robust Optimization

Bo Zeng (University of Pittsburgh)

28/07/2025 16:30

(Distributionally) robust optimization

Invited talk

Distributionally robust optimization (DRO) has been recognized as a powerful optimization scheme to handle random factors in decision making. The mainstream solution methods are generally based on duality theory, which might be technically challenging and less intuitive. In this talk, we consider two-stage DRO from the primal perspective, and develop a corresponding decomposition algorithm...

224. A Primal Perspective on Distributionally Robust Two-Stage Problems with Integer Recourse

Yiling Zhang

28/07/2025 17:00

(Distributionally) robust optimization

Invited talk

In this talk, we introduce and study a two-stage distributionally two-stage linear problem with integer recourse, where the objective coefficients are random. The random parameters follow the worst-case distribution belonging to a second-order conic representable ambiguity set of probability distributions. We show that the worst-case recourse objective, under various risk measures, can be...

141. Distributionally robust optimization through the lens of submodularity

Arjun Ramachandra

28/07/2025 17:30

(Distributionally) robust optimization

Invited talk

Distributionally robust optimization is used to solve decision making problems under uncertainty where the distribution of the uncertain data is itself ambiguous. While several tractable models have been proposed for continuous uncertainty using a convex ambiguity set, fewer results are known for discrete uncertainty. In this work, we identify a class of distributionally robust optimization...

246. Solving two-stage distributionally robust optimization with mixed-integer ambiguity sets via a hybrid Benders and column-and-constraint decomposition method

Pascal Quach (CentraleSupelec)

28/07/2025 18:00

(Distributionally) robust optimization

Contributed talk

We propose a decomposition algorithm to approximately solve two-stage distributionally robust optimization problems with mixed-integer ambiguity sets. Such settings are particularly relevant in non-cooperative contexts, such as unplanned disruptions or interdictions, where an adversary reacts to the defender's decisions. DRO is a natural fit, but resulting problems are difficult to reformulate...

44. Robustness and Regret Tolerance

Yilin Xue (National University of Singapore)

29/07/2025 10:45

(Distributionally) robust optimization

Invited talk

This paper shifts focus from the typical approach of maximizing expected reward to minimizing expected regret, aiming to find a solution whose expected reward is close to the oracle. While these two approaches are equivalent when the uncertainty distribution is given, they diverge when accounting for distributional ambiguity, which is characterized by the Wasserstein distance, for enhanced...

272. Can Inverse Optimization Compete with Neural Networks?

Peyman Mohajerin Esfahani (University of Toronto)

29/07/2025 11:15

Machine learning

Invited talk

We study a class of learning models known as inverse optimization (IO), where the goal is to replicate the behaviors of a decision-maker (i.e., optimizer) with an unknown objective function. We discuss recent developments in IO concerning convex training losses and optimization algorithms. The main message of this talk is that IO is a rich learning model that can capture complex, potentially...

104. What’s hidden in the tails? Revealing and reducing optimistic bias in entropic risk estimation and optimization

Angelos Georghiou (University of Cyprus)

29/07/2025 11:45

(Distributionally) robust optimization

The entropic risk measure is commonly used in high-stakes decision-making to account for tail risks. Empirical entropic risk estimator that replaces expectation in the entropic risk measure with sample average underestimates true risk. To correct this bias, a strongly asymptotically consistent bootstrapping procedure is proposed that fits a distribution to the data and then estimates the bias...

50. Process Flexibility: A Distribution-Free Approach to Long Chain Resilience

LI CHEN

29/07/2025 14:00

Applications in energy, finance or logistics

Invited talk

Process flexibility has been a well-established supply chain strategy in both theory and practice that enhances responsiveness to demand uncertainty. In this study, we expand the scope of this strategy to supply disruption mitigation by analyzing a long chain system. Specifically, we investigate the effectiveness of long chains in the face of random supply disruptions and demand uncertainty....

48. Distribution-Free Auctions: Balancing Regret, Equity, and Opacity

Prof. Napat Rujeerapaiboon (National University of Singapore)

29/07/2025 14:30

(Distributionally) robust optimization

Invited talk

We study a mechanism design problem where a seller aims to allocate a good to multiple bidders, each with a private value. The seller supports or favors a specific group, referred to as the minority group. Specifically, the seller requires that allocations to the minority group are at least a predetermined fraction (equity level) of those made to the rest of the bidders. Such constraints...

115. From Data to Donations: Optimal Fundraising Campaigns for Non-Profit Organizations

Zhengchao Wang (Imperial College London)

29/07/2025 15:00

(Distributionally) robust optimization

Invited talk

Non-profit organizations play a vital role in addressing global challenges, yet their financial sustainability often hinges on the effectiveness of their fundraising campaigns. We collaborate with a major international non-profit organization to develop and test data-driven approaches to increase the efficiency of their fundraising efforts. Our partner organization conducts multiple annually...

142. Learning Robust Risk Scores from Observational Data under Unobserved Confounders

Cagil Kocyigit (University of Luxembourg)

29/07/2025 15:30

(Distributionally) robust optimization

Invited talk

We consider the problem of learning, from observational data, a logistic regression model to predict the risk of an adverse outcome under no treatment. These problems arise routinely in public health and the social sciences, e.g., to help prioritize individuals for scarce resources or services. The vast majority of the literature on the topic assumes unconfoundedness, i.e., no unobserved...

41. Robust chance-constrained optimization with discrete distributions via a bundle approach

Daniela Bernhard (Friedrich-Alexander Universität Erlangen-Nürnberg)

30/07/2025 10:45

(Distributionally) robust optimization

Contributed talk

Typically, probability distributions that generate uncertain parameters are uncertain themselves or even unknown. Distributional robustness determines optimized decisions that are protected in a robust fashion against all probability distributions in some appropriately chosen ambiguity set. We consider robust joint chance-constrained optimization problems with discrete probability...

174. Nonlinear Decision Rules Made Scalable by Nonparametric Liftings

Eojin Han (University of Notre Dame)

30/07/2025 11:15

(Distributionally) robust optimization

Contributed talk

Sequential decision making often requires dynamic policies, which are computationally not tractable in general. Decision rules provide approximate solutions by restricting decisions to simple functions of uncertainties. In this paper, we consider a nonparametric lifting framework where the uncertainty space is lifted to higher dimensions to obtain nonlinear decision rules. Current...

30. Data-driven interdiction with asymmetric cost uncertainty: a distributionally robust optimization approach

Sergei Ketkov (Department of Business Administration, University of Zurich)

30/07/2025 11:45

(Distributionally) robust optimization

Contributed talk

We consider a class of stochastic interdiction games between an upper-level decision-maker (referred to as a leader) and a lower-level decision-maker (referred to as a follower), where the follower's objective function coefficients are subject to uncertainty.
More specifically, unlike traditional deterministic interdiction problem settings, the follower's profits (or costs) in our model...

175. Fixed Interval Scheduling with Random Delays: Distributionally Robust and Multi-stage Approaches

Monika Matoušková

30/07/2025 12:15

(Distributionally) robust optimization

Contributed talk

In logistics and transportation, scheduling tasks with fixed start times is a common challenge, often complicated by unpredictable delays. To tackle these issues, we need robust optimization techniques that can adapt to real-world uncertainties.
Initially, we explore an operational FIS problem where job completion times are influenced by random delays, modeled using Archimedean copulas to...

93. Condition Number Shrinkage by Joint Distributionally Robust Covariance-Precision Estimation

M. Renjie Chen (The Chinese University of Hong Kong)

01/08/2025 10:45

(Distributionally) robust optimization

Contributed talk

We propose a distributionally robust formulation for the simultaneous estimation of the covariance and precision matrix of a random vector. The proposed model minimizes the worst-case weighted sum of the Stein's loss of the precision matrix estimator and the Frobenius loss of the covariance estimator against all distributions from an ambiguity set centered at the empirical distribution. The...

87. Solving Wasserstein Distributionaly Robust Optimization by regularization

Florian Vincent (Inria Grenoble, Laboratoire Jean Kuntzmann)

01/08/2025 11:15

(Distributionally) robust optimization

Contributed talk

Distributionaly robust optimization with Wassersein-distance uncertainty sets proves to be an outstanding tool to handle data heterogeneity and distribution shifts; see (Kuhn et al.)[2].
Recently, (Azizian et al.)[1] studied regularizations of WDRO problems. From a risk minimization problem $\min_{\theta\in\Theta}\mathbb{E}_\xi[\ell_\theta(\xi)]$ (ERM), it provides a dual formula (WDRO)...

147. A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization

Simon Thomä (RWTH Aachen)

01/08/2025 11:45

(Distributionally) robust optimization

Contributed talk

Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model managerial problems. Given the significant computational challenges involved, these problems are typically solved approximately. This short note introduces an algorithmic framework that revisits a popular approximation scheme...

161. A Distributionally Robust Optimization Framework with Regularization Control

Diego Fonseca Valero (Universidad EAFIT)

01/08/2025 12:15

(Distributionally) robust optimization

Contributed talk

We propose a novel Wasserstein distributionally robust optimization (DRO) framework with regularization control that naturally leads to a family of regularized problems with user‐controllable penalization mechanisms. Our approach bridges the gap between conventional DRO formulations and practical decision-making by explicitly incorporating adverse scenario information into the optimization...

181. Data-driven Mixed Probabilistic and Possibilistic Distributionally Robust Optimization: a Case Study for Joint Renewable Energy and Energy Storage Planning

Dr Abolhassan Mohammadi Fathabad (Postdoc at the School of Industrial Engineering, University of Tehran)

(Distributionally) robust optimization

Contributed talk

It is often the case where historical data used to represent uncertainty need some expert-based opinion before being suitable for use in a planning problem. For instance, in a renewable energy and energy storage planning (REESP) problem, the solar generation data may be gathered using older generation of solar panel technology while the nowel solar panels intended for planning have improved...