We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of the literature focuses on learning only the objective function, as learning the constraint function (i.e., feasible regions) leads to nonconvex training programs. Motivated by this, we focus on … Read more
Maintenance strategies are pivotal in ensuring the reliability and performance of critical components within industrial machines and systems. However, accurately determining the optimal replacement time for such components under stress and deterioration remains a complex task due to inherent uncertainties and variability in operating conditions. In this paper, we propose a comprehensive approach based on … Read more
With food waste levels of about 30%, mostly caused by overproduction, reducing food waste poses an important challenge in the food service sector. As food is prepared in advance rather than on demand, there is a significant risk that meals or meal components remain uneaten. Flexible meal planning can promote the reuse of these leftovers … Read more
We study continuous quadratic submodular minimization with bounds and propose a polynomially sized semidefinite relaxation, which is provably tight for dimension n ≤ 3 and empirically tight for larger n. We apply the relaxation to two moment problems arising in distributionally robust optimization and the computation of covariance bounds. Accordingly, this research advances the ongoing … Read more
We study offline reinforcement learning problems with a long-run average reward objective. The state-action pairs generated by any fixed behavioral policy thus follow a Markov chain, and the empirical state-action-next-state distribution satisfies a large deviations principle. We use the rate function of this large deviations principle to construct an uncertainty set for the unknown true … Read more
Both bilevel and robust optimization are established fields of mathematical optimization and operations research. However, only until recently, the similarities in their mathematical structure has neither been studied theoretically nor exploited computationally. Based on the recent results by Goerigk et al. (2025), this paper is the first one that provides an extensive computational study for … Read more
We introduce Mixed Integer Linear Programming (MILP) formulations for the two-stage robust surgery scheduling problem (2SRSSP). We derive these formulations by modeling the second-stage problem as a longest path problem on a layered acyclic graph and subsequently converting it into a linear program. This linear program is then dualized and integrated with the first-stage, resulting … Read more
We consider the problem of computing the optimal solution and objective of a linear program under linearly changing linear constraints. The problem studied is given by $\min c^t x \text{ s.t } Ax + \lambda Dx \leq b$ where $\lambda$ belongs to a set of predefined values $\Lambda$. Based on the information given by a … Read more
In this paper, we study the two-stage distributionally robust optimization (DRO) problem from the primal perspective. Unlike existing approaches, this perspective allows us to build a deeper and more intuitive understanding on DRO, to leverage classical and well established solution methods and to develop a general and fast decomposition algorithm (and its variants), and to … Read more
Two-stage robust optimization (two-stage RO), due to its ability to balance robustness and flexibility, has been widely used in various fields for decision-making under uncertainty. This paper proposes a multiple kernel learning (MKL)-aided column-and-constraint generation (CCG) method to address this issue in the context of data-driven decision optimization, and releases a corresponding registered Julia package, … Read more