We discuss a new approach to asset allocation with transaction costs. A multi-period stochastic linear programming model is developed where the risk is based on the worst case payoff which is endogenously determined by the ...

We present a dynamic multistage stochastic programming model for the cost-optimal generation of electric power in a hydro-thermal system under uncertainty in load, inflow to reservoirs and prices for fuel and delivery ...

In this paper we study a modifcation of the well-known simulated annealing method, adapting it to discrete stochastic optimization problems. Our algorithm is based on a variable-sample Monte Carlo technique, in which the ...

Production planning has a fundamental role in any manufacturing operation. The problem is to decide what type of, and how much, product should be produced in future time periods. The decisions should be based on many ...

The problem of adaptive routing in a network with failures is considered. The network may be in one of finitely many states characterized by different travel times along the arcs, and transitions between the states occur ...

We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. The ...

An ergodic theorem for random lsc functions is obtained by relying on a (novel) 'scalarization' of such functions. In the process, Kolmogorov's extension theorem for randon lsc functions is established. Applications to ...

The purpose of this paper is to demonstrate how to evaluate stochastic programming models, and more specifically to compare two different approaches to asset liability management. The first uses multistage stochastic ...

This paper considers the problem of continuous investment of capital in risky assets over time. Using a Bayesian framework, a model for asset prices is developed where the current price dynamics depend on the history of ...

Random lsc (lower semicontinuous) functions can be indentified with a vector-valued random variable by means of an appropriate scalarization. It is shown that stationarity, ergodicity and independence properties are preserved ...