Browsing Stochastic Programming Eprint Series (SPEPS) by Title
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20031020BuchEfficient point methods for probabilistic optimization problems We consider nonlinear stochastic programming problems with probabilistic constraints. The concept of a pefficient point of a probability distribution is used to derive equivalent problem formulations, and necessary and ...

20130513BuchElectricity Swing Option Pricing by Stochastic Bilevel Optimization: a Survey and New Approaches We demonstrate how the problem of determining the ask price for electricityswing options can be considered as a stochastic bilevel program with asymmetricinformation. Unlike as for financial options, there is no way for ...

20040210BuchEpiconvergent discretizations of multistage stochastic programs In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled as random variables with an infinite support. This results in infinitedimensional optimization problems that can rarely ...

20030725BuchEpiconvergent discretizations of stochastic programs via integration quadratures Modern integration quadratures are designed to produce finitely supported approximations of a given (probability) measure. This makes them well suited for discretization of stochastic programs. We give conditions that ...

20080405BuchEpiconvergent scenario generation method for stochastic problems via sparse grid One central problem in solving stochastic programming problems is to generate moderatesized scenario trees which represent well the risk faced by a decision maker. In this paper we propose an efﬁcient scenario generation ...

20060309BuchEstimation method of multivariate exponential probabilities based on a simplex coordinates transform A novel unbiased estimator for estimating the probability mass of a multivariate exponential distribution over a measurable set is introduced and is called the Exponential Simplex (ES) estimator. For any measurable set, ...

20030714BuchEvaluation of scenariogeneration methods for stochastic programming In this paper, we discuss the evaluation of quality/suitability of scenariogeneration methods for a given stochastic programming model. We formulate minimal requirements that should be imposed on a scenariogeneration ...

20020422BuchExact solutions to a class of stochastic generalized assignment problems This paper deals with a stochastic Generalized Assignment Problem with recourse. Only a random subset of the given set of jobs will require to be actually processed. An assignment of each job to an agent is decided a priori, ...

20050411BuchExtending algebraic modelling languages for Stochastic Programming The algebraic modelling languages (AML) have gained wide acceptance and use in Mathematical Programming by researchers and practitioners. At a basic level, stochastic programming models can be defined using these languages ...

20090522BuchFenchel Decomposition for Stochastic MixedIntegerProgramming This paper introduces a new cutting plane method for twostage stochastic mixedinteger programming (SMIP) called Fenchel decomposition (FD). FD usesa class of valid inequalities termed, FD cuts, which are derived based ...

20000216BuchFinite capacity production planning with random demand and limited information 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 ...

20021024BuchFrontiers of stochastically nondominated portfolios We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose meanrisk models which are solvable by linear programming and generate ...

20060320BuchGenetic algorithm based technique for solving chance constrained problems Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance Constrained Programming (CCP) have been used for modelling and analysis of ...

20120220BuchGradient estimates for Gaussian distribution functions: Application to probabilistically constrained optimization problems We provide lower estimates for the norm of gradients of Gaussian distribution functions and apply the results obtained to a special class ofprobabilistically constrained optimization problems. In particular, it is shown ...

19991220BuchHedging electricity portfolios via stochastic programming Electricity producers participating in the Nordic wholesalelevel market face significant uncertainty in inflow to reservoirs and prices in the spot and contract markets. Taking the view of a single riskaverse producer, ...

20020503BuchHigherOrder Upper Bounds on the Expectation of a Convex Function We develop a decreasing sequence of upper bounds on the expectation of a convex function. The nth term in the sequence uses moments and crossmoments of up to degree n from the underlying random vector. Our work has ...

20030306BuchHölder and Lipschitz Stability of Solution Sets in Programs with Probabilistic Constraints We study perturbations of a stochastic program with a probabilistic constraint and $r$concave original probability distribution. First we improve our earlier results substantially and provide conditions implying Hölder ...

20020729BuchIntegrated chance constraints reduced forms and an algorithmWe consider integrated chance constraints (ICC), which provide quantitative alternatives for traditional chance constraints. We derive explicit polyhedral descriptions for the convex feasible sets induced by ICCs, for the ...

20030704BuchIntegrated chance constraints in an ALM model for pension funds We discuss integrated chance constraints in their role of shortterm risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for ...

20020604BuchIntegration quadratures in discretization of stochastic programs Because of its simplicity, conditional sampling is the most popular method for generating scenario trees in stochastic programming. It is based on approximating probability measures by empirical ones generated by random ...