In this paper, we define and study a new class of optimal stochastic control problems which is closely related to the theory of Backward SDE's and forward-backward SDE's. The controlled process takes values in RXR and a given initial data for X(O). Then, the control problem is to find the...

Sequential meta heuristic implementations for the irregular stock-cutting problem have highlighted a number of common problems. The implementation in this paper adopts a hybrid tabu search approach that incorporates two very different optimisation routines which utilise alternative neighborhoods...

The Envelope Theorem is showm to involve comparisons between the performance of the optimal policy and that of alternative policies, which satisfy the constraints of the problem and may also have other features. The theorem is activated whenever parameter values are such that the extreme value...

A standard approach to duality in stochastic optimization problems with constraints in L(infinite) relies upon the Yosida-Hewitt theorem. We develop an alternative technique which employs only "elementary" means. The technique is based on an e-regularization of the original problem and on...

This paper develops a formal framework based on multivariate spectral techniques for assesssing the performance of multivariate dynamic models whose solution is approximated through simulation. The approach is especially suitable for models that focus on a particular frequency range , such as...

In this paper we study the concepts of equilibrium and optimum in static transportation networks with elastic and non-elastic demands. The main mathematical tool of our paper is the theory of variational inequalities. We demonstrate that this theory is useful for proving the existence theorems....

In this paper we consider a labor constrained scheduling problem (LCSP) which is a simplification of a practical problem arising in industry. Jobs are subject to precedence constraints and have specified processing times. Moreover, for each job the labor requirements varies as the job is...

We present a survey of nondifferentiable optimization problems and methods with special focus on the analytic center cutting plane method. We propose a self-contained convergence analysis, that uses the formalism of the theory of self-concordant fucntions, but for the main results, we give...

We propose an alternative apporach 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 associated objectiev value is doubly random, since it depends two...

New results in the asymptotic theory of Markov processes are applied to analysis of the long-run behaviour exhibited by optimal growth models with unbounded productivity shock. The techniques developed here are geometrically intuitive, and are shown to imply global stability for a popular model...