We study the problem of integrated staffing and scheduling under demand uncertainty. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. The here-and-now decision is to find initial staffing levels and schedules. The wait-and-see decision is to adjust these schedules at a time closer to the actual date of demand realization. We show that the mixed-i...

An analysis of convex stochastic programs is provided when the underlying probability distribution is subjected to (small) perturbations. It is shown, in particular, that ε-approximate solution sets of convex stochastic programs behave Lipschitz continuously with respect to certain distances of probability distributions that are generated by the relevant integrands. It is shown that these resul...

This paper applies two-stage stochastic quadratic programming to optimize conjunctive use operations of groundwater pumping and artificial recharge with farmer’s expected revenue and cropping decisions. The two-stage programming approach allows modeling of water and permanent crop production decisions, with recourse for uncertain conditions of hydrology, annual crops, and irrigation technology ...

This paper studies a Stochastic Multi-Period Location-Transportation Problem (SMLTP) characterized by multiple transportation options, multiple demand periods and a stochastic stationary demand. We consider the determination of the number and location of the depots required to satisfy a set of customer’s demands, and the mission of these depots in terms of the subset of customers they must supp...

The availability of intra-day data on the prices of speculative assets means that we can use quadratic variation like measures of activity in financial markets, called realised volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realised volatility erro...

We present a logical framework to represent and reason about stochastic optimization problems based on probability answer set programming [Saad and Pontelli, 2006; Saad, 2006; Saad, 2007a]. This is established by allowing probability optimization aggregates, e.g., minimum and maximum in the language of probability answer set programming to allow minimization or maximization of some desired crit...

We study quantitative stability of linear multistage stochastic programs under perturbations of the underlying stochastic processes. It is shown that the optimal values behave Lipschitz continuous with respect to an Lp-distance. Therefor, we have to make a crucial regularity assumption on the conditional distributions, that allows to establish continuity of the recourse function with respect to...

Static and stochastic vehicle routing problems (SS-VRP) aim at modeling and solving real life problems by considering uncertainty on the data. In particular, customer data may not be known with certainty. Before the beginning of the day, probability distributions on customer data are used to compute a firststage solution that optimizes an expected cost. Customer data are revealed online, while ...

Linear stochastic programming provides a flexible toolbox for analyzing reallife decision situations, but it can become computationally cumbersome when recourse decisions are involved. The latter are usually modeled as decision rules, i.e., functions of the uncertain problem data. It has recently been argued that stochastic programs can quite generally be made tractable by restricting the space...

ddsip.vSD is a C-implementation of a number of scenario decomposition algorithms for stochastic linear programs with firstor second-order stochastic dominance constraints induced by mixed-integer linear recourse. The program is based on a previous implementation of scenario decomposition algorithms for mean-risk models of A. Märkert [20]. Main idea of the decomposition algorithms is the Lagrang...