Biao Han – Optimization Online

Robust Out-of-Distribution Stochastic Optimization Based on Wasserstein-Metric Meta-Distribution Support Learning

Published: 2026/10/05, Updated: 2026/10/06

Biao Han

Optimization in Data Science, Robust Optimization, Stochastic Programming bilevel optimization, convex optimization, data-driven stochastic programming, distributionally robust optimization, Machine learning and optimization, robust optimization

We study stochastic optimization with a completely unobserved target distribution and samples from related source distributions. Treating the sources and the target as independent draws from a common meta-distribution, we propose Wasserstein-metric meta-distribution support learning (MDSL). The model jointly learns the center and radius of a Wasserstein ball, with an exclusion parameter controlling the fraction … Read more

Robust Out-of-Distribution Stochastic Optimization with Heterogeneous Inclusive and Exclusive Distribution Clusters

Published: 2026/09/10, Updated: 2026/09/18

Biao Han

Infinite Dimensional Optimization, Robust Optimization, Stochastic Programming Biconvex optimization, bilevel optimization, cognitive science, convex optimization, Data-Driven Robust Optimization, decision science, distributionally robust optimization (DRO), Machine learning and optimization, multiple kernel learning

Decision scenarios far from rare in practice often place data-driven decision-making in an awkward position, where the decision maker may have access to neither the target distribution itself nor any empirical samples drawn from it, especially when decisions must be made in novel or highly uncertain environments. In response, robust out-of-distribution stochastic optimization (RooDSO) has … Read more

Data-Driven Multistage Scheduling Optimization for Refinery Production under Uncertainty: Systematic Framework, Modeling Approach, and Application Analysis

Published: 2025/05/29

Biao Han

Control Applications, Scheduling, Stochastic Programming control and scheduling, data-driven, julia, mutistage, refinery scheduling, reinforcement learning, stochastic programming, uncertainty

The widespread existence of various uncertainties makes the inherently complex refinery production scheduling problem even more challenging. To address this issue, this paper proposes a viable systematic data-driven multistage scheduling optimization framework and develops a corresponding structured modeling methodology. Under this paradigm, unit-level advanced control and plant-level intelligent scheduling are coordinated to jointly deal with … Read more

Multiple Kernel Learning-Aided Column-and-Constraint Generation Method

Published: 2024/12/26, Updated: 2024/12/28

Biao Han

Applications - OR and Management Sciences, Optimization in Data Science, Robust Optimization column-and-constraint generation, julia, machine learning, multiple kernel learning, two-stage robust optimization

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