We study the canonical problem of maximizing a stochastic submodular function subject to cardinality constraint, where goal is select subset from ground set items with uncertain individual performances maximize their expected group value. Although near-optimal algorithms have been proposed for this problem, practical concerns regarding scalability, compatibility distributed implementation, and ...

existence of capacity drop phenomenon, as the difference between pre-queue and queue discharge flow rates, has been one of the controversial concepts of traffic engineering. several researches have focused on capacity drop existence and also its estimation issues. this paper aims to estimate capacity drop based not only on a comparison between breakdown and queue discharge flow rates, but also ...

Stochastic programming is an optimization approach taking into account uncertainties in the system model. There are numerous possible applications of stochastic programming. The purpose of this short report is to introduce stochastic programming in simple, tutorial-like, terms. A simple example of an optimization of a covering gas demand is provided together with pointing out some fundamental p...

In this paper we study optimization problems with second-order stochastic dominance constraints. This class of problems allows for the modeling of optimization problems where a riskaverse decision maker wants to ensure that the solution produced by the model dominates certain benchmarks. Here we deal with the case of multi-variate stochastic dominance under general distributions and nonlinear f...

A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulati...

this paper proposes a stochastic framework for demand response (dr) aggregator to procure dr from customers and sell it to purchasers in the wholesale electricity market. the aggregator assigns fixed dr contracts with customers based on three different load reduction strategies. in the presented problem the uncertainty of market price is considered and the risk of aggregator participation is ma...

We study a stochastic control problem for the optimization of observations in a partially observable stochastic system. Using a method of discontinuous time transformation, we associate with the original problem with unbounded controls a problem that has bounded controls. This latter problem allows us to construct nearly optimal nonanticipative Lipschitz Markov controls with finite observation ...

AND

In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We ...