We consider totally unimodular multistage stochastic programs, that is, multistage stochastic programs whose extensive-form constraint matrices are totally unimodular. We establish several sufficient conditions and identify examples that have arisen in the literature.

Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fi...

We present a technique to associate to stochastic programs written in stochastic Concurrent Constraint Programming a semantics in terms of a lattice of hybrid automata. The aim of this construction is to provide a framework to approximate the stochastic behavior by a mixed discrete/continuous dynamics with a variable degree of discreteness.

This paper introduces a new approach for scheduling security constraint unit commitment (SCUC) including wind farms. Because of uncertainty in wind power production, we tried to develop a new method for incorporating wind power generation in power plant scheduling. For this, wind power generation modeled with unit commitment in a non-linear optimization problem and simulated by submitting diffe...

We propose a prior probability model for two distributions that are ordered according to a stochastic precedence constraint, a weaker restriction than the more commonly utilized stochastic order constraint. The modeling approach is based on structured Dirichlet process mixtures of normal distributions. Full inference for functionals of the stochastic precedence constrained mixture distributions...

We are interested in risk constraints for infinite horizon discrete time Markov decision processes (MDPs). Starting with average reward MDPs, we show that increasing concave stochastic dominance constraints on the empirical distribution of reward lead to linear constraints on occupation measures. An optimal policy for the resulting class of dominance-constrained MDPs is obtained by solving a li...

This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich’s OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. This formulation arises naturally when decisions are restricted by stochastic e...

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost function in terms of expected values and higher moments of the states, and chance constraints that ensure probabilistic constraint satisfaction. The general...

In this paper, we propose Distributed Mirror Descent (DMD) algorithm for constrained convex optimization problems on a (strongly-)connected multi-agent network. We assume that each agent has a private objective function and a constraint set. The proposed DMD algorithm employs a locally designed Bregman distance function at each agent, and thus can be viewed as a generalization of the well-known...

We make a novel connection between an energy-efficient wireless transmission scheduling problem and a multi-period, multi-item inventory control problem with stochastic convex ordering costs, deterministic demands, and a joint budget constraint. In the special case of a single item, we show that a state-dependent modified base-stock policy is optimal when the stochastic ordering costs are linea...