Distributionally Robust Universal Classification: Bypassing the Curse of Dimensionality

The Universal Classification (UC) problem seeks an optimal classifier from a universal policy space to minimize the expected 0-1 loss, also known as the misclassification risk. However, the conventional empirical risk minimization often leads to overfitting and poor out-of-sample performance. To address this limitation, we introduce the Distributionally Robust Universal Classification (DRUC) formulation, which incorporates … Read more

Investment and Operational Planning for an electric market with massive entry of renewable energy

In this paper, we study a joint problem in which the Independent System Operator (ISO) intends to minimize the joint cost of operation and investment in a network structure. The problem is formulated through operational and investment control variables; we discuss the hierarchy between them and use the so-called Day Ahead Problem to find an … Read more

First-order methods for stochastic and finite-sum convex optimization with deterministic constraints

In this paper, we study a class of stochastic and finite-sum convex optimization problems with deterministic constraints. Existing methods typically aim to find an \(\epsilon\)-expectedly feasible stochastic optimal solution, in which the expected constraint violation and expected optimality gap are both within a prescribed tolerance ϵ. However, in many practical applications, constraints must be nearly … Read more

A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming

The transition to clean energy systems at large-scale campuses is a critical step toward achieving global decarbonization goals. However, this transition poses significant challenges, including substantial capital requirements, technological uncertainties, and the operational complexities of integrating renewable energy technologies. This study presents a dynamic strategic planning framework for campus-scale clean electricity transitions, utilizing a multi-stage … Read more

Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance

Recent research increasingly integrates machine learning (ML) into predictive maintenance (PdM) to reduce operational and maintenance costs in data-rich operational settings. However, uncertainty due to model misspecification continues to limit widespread industrial adoption. This paper investigates a PdM framework in which sensor-driven prognostics inform decision-making under economic trade-offs within a finite decision space. We investigate … Read more

The L-Shaped Method for Stochastic Programs with Decision-Dependent Uncertainty

In this paper we extend the well-known L-Shaped method to solve two-stage stochastic programming problems with decision-dependent uncertainty. The method is based on a novel, unifying, formulation and on distribution-specific optimality and feasibility cuts for both linear and integer stochastic programs. Extensive tests on three production planning problems illustrate that the method is extremely effective … Read more

Algorithmic Approaches for Identifying the Trade-off between Pessimism and Optimism in a Stochastic Fixed Charge Facility Location Problem

We introduce new algorithms to identify the trade-off (TRO) between adopting a distributional belief and hedging against ambiguity when modeling uncertainty in a capacitated fixed charge facility location problem (CFLP). We first formulate a TRO model for the CFLP (TRO-CFLP), which determines the number of facilities to open by minimizing the fixed establishment cost and … Read more

ASMOP: Additional sampling stochastic trust region method for multi-objective problems

We consider an unconstrained multi-criteria optimization problem with finite sum objective functions. The proposed algorithm belongs to a non-monotone trust-region framework where additional sampling approach is used to govern the sample size and the acceptance of a candidate point. Depending on the problem, the method can result in a mini-batch or an increasing sample size … Read more

Statistical Inference for Distributed Contextual Multi-armed Bandit

In this paper, we study the online statistical inference of distributed contextual multi-armed bandit problems, where the agents collaboratively learn an optimal policy by exchanging their local estimates of the global parameters with neighbors over a communication network. We propose a distributed online decision making algorithm, which balances the exploration and exploitation dilemma via the … Read more

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

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