A novel parallel decomposition algorithm is developed for large, multistage stochastic
optimization problems. The method decomposes the problem into subproblems that correspond to …

Mathematical programming models with noisy, erroneous, or incomplete data are common
in operations research applications. Difficulties with such data are typically dealt with …

… I was pleased to have John Mulvey join me as a co-editor of this volume. Besides co-authoring
the introduction with me, John adds his special insights gained from years of outstanding …

We employ model predictive control for a multi-period portfolio optimization problem. In
addition to the mean-variance objective, we construct a portfolio whose allocation is given by …

Several financial planning problems are posed as dynamic generalized network models
with stochastic parameters. Examples include: asset allocation for portfolio selection, …

This paper presents two effective algorithms for clustering n entities into p mutually exclusive
and exhaustive groups where the ‘size’ of each group is restricted. As its objective, the …

This paper presents and tests an effective optimization algorithm for clustering homogeneous
data. The algorithm iteratively employs a subgradient method for determining lower …

The authors present a machine learning approach to regime-based asset allocation. The
framework consists of two primary components: (1) regime modeling and prediction and (2) …

The problem of designing contaminated groundwater remediation systems using hydraulic
control is addressed. Two nonlinear optimization formulations are proposed which model the …

The introduction of uncertainty to mathematical programs greatly increases the size of the
resulting optimization problems. Specialized methods that exploit program structures and …