Stochastic Programming – Page 11

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

Published: 2025/06/13

Facility Planning and Design, Robust Optimization, Stochastic Programming distributionally robust optimization, facility location, stochastic programming, uncertainty

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

Published: 2025/06/12, Updated: 2026/03/12

Multi-Criteria Optimization, Optimization of Systems modeled by PDEs, Stochastic Programming adaptive sample size, Additional sampling, multi-objective optimization, non-monotone trust region, Pareto critical points, stochastic convergence

We consider unconstrained multi-criteria optimization problems 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 yield a mini-batch or an increasing sample size behavior. … Read more

Statistical Inference for Distributed Contextual Multi-armed Bandit

Published: 2025/06/06

Stochastic Programming $\varepsilon$-greedy, confidence intervals, distributed contextual multi-armed bandit, distributed stochastic gradient descent, random scaling

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

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

On Multi-Disjunctive Valid Inequalities for Chance-Constrained Stochastic Problems with Finite Support

Published: 2025/05/26, Updated: 2026/05/04

(Mixed) Integer Linear Programming, Stochastic Programming chance constraints, Closures, Mixed-Integer Linear Optimization, stochastic optimization, valid inequalities

We consider mixed-integer linear chance-constrained problems for which the random vector that parameterizes the feasible region has finite support. Our key objective is to improve branch-and-bound or -cut approaches by introducing new types of valid inequalities that improve the dual bound and, by this, the overall performance of such methods. We introduce so-called multi-disjunctive valid … Read more

Multi-cut stochastic approximation methods for solving stochastic convex composite optimization

Published: 2025/05/22, Updated: 2026/03/02

Convex Optimization, Stochastic Programming optimal complexity bound, proximal bundle method, stochastic approximation, stochastic convex composite optimization

This paper considers the stochastic convex composite optimization problem and presents multi-cut stochastic approximation (SA) methods for solving it, whose models in expectation overestimate its objective function. The multi-cut model obtained by taking the maximum of a finite number of linearizations of the stochastic objective function provides a biased estimate of the objective function, with … Read more

Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization

Published: 2025/05/15, Updated: 2025/05/16

Rui Gao

Stochastic Programming

We study convergence properties of the discrete-time Mean-Field Langevin Stochastic Descent-Ascent (MFL-SDA) algorithm for solving distributional minimax optimization. These problems arise in various applications, such as zero-sum games, generative adversarial networks and distributionally robust learning. Despite the significance of MFL-SDA in these contexts, the discrete-time convergence rate remains underexplored. To address this gap, we establish … Read more

An Adaptive Stochastic Dual Progressive Hedging Algorithm for Stochastic Programming

Published: 2025/05/07

Stochastic Approaches, Stochastic Programming Adaptive Sequential Sampling, progressive hedging algorithm, Stochastic Conjugate Subgradient Method, stochastic programming

The Progressive Hedging (PH) algorithm is one of the cornerstones in large-scale stochastic programming. However, its traditional development requires that all scenario subproblems are solved per iteration, and a probability distribution with finitely many outcomes. This paper introduces a stochastic dual PH algorithm (SDPH) to overcome these challenges. We introduce an adaptive sampling process and … Read more

A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints

Published: 2025/04/29, Updated: 2025/05/01

Branch and Cut Algorithms, Scheduling, Stochastic Programming chance constraints, decision diagrams, scheduling

The Chance-Constrained Parallel Machine Scheduling Problem (CC-PMSP) assigns jobs with uncertain processing times to machines, ensuring that each machine’s availability constraints are met with a certain probability. We present a decomposition approach where the master problem assigns jobs to machines, and the subproblems schedule the jobs on each machine while verifying the solution’s feasibility under … Read more

A Survey on the Applications of Stochastic Dual Dynamic Programming and its Variants

Published: 2025/04/28

Dynamic Programming, Integer Programming, Stochastic Programming approximate dynamic programming, stochastic dual dynamic integer programming (sddip), stochastic dual dynamic programming, stochastic programming

Stochastic Dual Dynamic Programming (SDDP) is widely recognized as the predominant methodology for solving large-scale multistage stochastic linear programming (MSLP) problems. This paper aims to contribute to the extant literature by conducting a comprehensive survey of the literature on SDDP within the realm of practical applications. We systematically identify and analyze the various domains where … Read more