Using the WOWA criterion for two-stage decision making problems

On Markov Decision Processes with Borel Spaces and an Average Cost Criterion

We consider average-cost Markov decision processes (MDPs) with Borel state and action spaces and universally measurable policies. For the nonnegative cost model and an unbounded cost model, we introduce a set of conditions under which we prove the average cost optimality inequality (ACOI) via the vanishing discount factor approach. Unlike most existing results on the ACOI, which require compactness/continuity conditions on the MDP, our result does not and can be applied to problems with discontinuous dynamics and one-stage costs. The key idea here is to replace the compactness/continuity condi…

SDDmiP.jl：一款带有可证收敛Benders算法的多阶段随机混合整数规划软件包

SDDmiP.jl: A Software Package with a Provably Convergent Benders Algorithm for Multi-Stage Stochastic Mixed-Integer Programming

本研究开发了开源软件包SDDmiP.jl，实现带ReLU割的可证收敛Benders型分解算法，结合两种割选择策略与交替割准则，经四类多阶段随机整数规划实验验证了方法性能。

一种求解稀疏广义特征值问题的逐次两阶段方法

A Successive Two-stage Method for Sparse Generalized Eigenvalue Problems

针对传统稀疏广义特征值问题（sGEP）算法易陷局部最优、依赖初始点的问题，提出含支撑变更机制的逐次两阶段循环方法，可获得更优目标值，为统计学习与优化提供有效工具。

探索用于Lagrange-Newton优化的替代线搜索方法

Exploring an Alternative Line-Search Method for Lagrange-Newton Optimization

本文提出了一种基于牛顿步向量场发散性的替代线搜索方法，用于解决Lagrange-Newton优化中的狭窄山谷步长阻尼问题。

利用集成学习的Benders分解加速L型两阶段随机SCUC

Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition

本文提出三种基于机器学习的Benders分解增强变体，通过回归和分类方法生成更紧的割并精简主问题，有效降低了两阶段随机SCUC的计算成本与内存使用。

Bi-objective facility location under uncertainty with an application in last-mile disaster relief

Multiple and usually conflicting objectives subject to data uncertainty are main features in many real-world problems. Consequently, in practice, decision-makers need to understand the trade-off between the objectives, considering different levels of uncertainty in order to choose a suitable solution. In this paper, we consider a two-stage bi-objective single source capacitated model as a base formulation for designing a last-mile network in disaster relief where one of the objectives is subject to demand uncertainty. We analyze scenario-based two-stage risk-neutral stochastic programming, ada…