A methodology, based on the concept of Affinely Adjustable Robust Optimization, for optimizing daily operation of pumping stations is proposed, which takes into account the fact that a water distribution system in reality is unavoidably affected by uncertainties. For operation control, the main source of uncertainty is the uncertainty in the demand. Traditional methods for optimizing dynamical ...

A model of multistage stochastic programming over a scenario tree is developed in which the evolution of information states, as represented by the nodes of a scenario tree, is supplemented by a dynamical system of state vectors controlled by recourse decisions. A dual problem is obtained in which multipliers associated with the primal dynamics are price vectors that are propagated backward in t...

Numerical approaches are developed for solving large-scale problems of extended linear-quadratic programming that exhibit Lagrangian separability in both primal and dual variables simultaneously. Such problems are kin to large-scale linear complemen-tarity models as derived from applications of variational inequalities, and they arise from general models in multistage stochastic programming and...

In this paper we study a single-sink transportation problem in which the production capacity of the suppliers and the demand of the single customer are stochastic. Shipments are performed by capacitated vehicles, which have to be booked in advance, before the realization of the production capacity and the demand. Once the production capacity and the demand are revealed, there is an option to ca...