Anticipatory algorithms for online stochastic optimization have been shown very effective in a variety of areas, including logistics, reservation systems, and scheduling. For such applications which typically feature purely exogenous uncertainty, the one-step anticipatory algorithm was shown theoretically to be close to optimal when the stochasticity of the problem, measured by the anticipatory...

We introduce a stochastic dynamic optimization problem, where risk aversion is expressed by a stochastic ordering constraint. The constraint requires that a random reward sequence depending on our decisions dominates a given benchmark random sequence. The dominance is defined by discounting both processes with a family of discount sequences, and by applying a univariate order. We describe the g...

In this paper, a Stochastic Dynamic Facility Location Problem (SDFLP) is formulated. In the first part, an exact solution method based on stochastic dynamic programming is given. It is only usable for small instances. In the second part a Monte Carlo based method for solving larger instances is applied, which is derived from the Sample Average Approximation (SAA) method.

This paper studies dynamic stochastic optimization problems parametrized by a random variable. Such problems arise in many applications in operations research and mathematical finance. We give sufficient conditions for the existence of solutions and the absence of a duality gap. Our proof uses extended dynamic programming equations, whose validity is established under new relaxed conditions tha...

We present experimental results about learning function values (i.e. Bellman values) in stochastic dynamic programming (SDP). All results come from openDP (opendp.sourceforge.net), a freely available source code, and therefore can be reproduced. The goal is an independent comparison of learning methods in the framework of SDP. 1 What is stochastic dynamic programming (SDP) ? We here very roughl...

1. Introduction Many important real-world problems contain stochastic elements and require optimization. Stochastic programming and simulation-based optimization are two approaches used to address this issue. We do not explicitly discuss other related areas including stochastic control, stochas-tic dynamic programming, and Markov decision processes. We consider a stochastic optimization problem...

Determination of facilities, such as factories or warehouses, location and availability conditions is one of the important and strategic decisions for an organization to make. Transportation costs that form a major part of goods price are dependent to this decision making. There are verity of methods have been presented to achieve the optimal locations of these facilities which are generally de...

the edge detour index polynomials were recently introduced for computing theedge detour indices. in this paper we nd relations among edge detour polynomials for the2-dimensional graph of tuc4c8(s) in a euclidean plane and tuc4c8(s) nanotorus.

2 Stochastic optimal control (discrete time) 2 2.1 The Linear-quadratic-Gaussian (LQG) case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Message passing in LQG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Special case: kinematic control . . . . . . . . . . . . . . . . . . . . . . . . . ...

An analysis of the dynamic behavior of Evolution Strategies applied to Trav-eling Salesman Problems is presented. For a special class of Traveling Salesman Problems a stochastic model of the optimization process is introduced. Based on this model diierent features determining the optimization process of Evolution Strategies are analyzed. In addition the stochastic model is extended to explain s...