Auflistung Stochastic Programming Eprint Series (SPEPS) nach Titel
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20070603BuchA BranchandBound Method for Multistage Stochastic Integer Programs with Risk Objectives We identify multistage stochastic integer programs with risk objectives where the related waitandsee problems enjoy similar separability as in the risk neutral case. For models belonging to this classwe present a solution ...

20040221BuchA branchandcut algorithm for the stochastic uncapacitated lotsizing problem This paper addresses a multistage stochastic integer programming formulation of the uncapacitated lotsizing problem under uncertainty. We show that the classical $(\mathcal{l}, S)$ inequalities for the deterministic ...

20061027BuchA branchandcut algorithm for twostage stochastic mixedbinary programs with continuous firststage variables This paper presents a branchandcut method for twostage stochastic mixedinteger programming (SMIP) problems with continuous firststage variables. This method is derived based on disjunctive decomposition(D2) for SMIP, ...

20021024BuchA branchandprice algorithm for multistage stochastic integer programming with application to stochastic batchsizing problems In this paper we present a branchandprice method to solve special structured multistage stochastic integer programming problems. We validate our method on two different versions of a multistage stochastic batchsizing ...

20050110BuchA BranchReduceCut Algorithm for the Global Optimization of Probabilistically Constrained Linear Programs We consider probabilistic constrained linear programs with general distributions for the uncertain parameters. These problems generally involve nonconvex feasible sets. We develop a branch and bound algorithm that searches ...

20041002BuchA class of stochastic programs with decision dependent uncertainty The standard approach to formulating stochastic programs is based on the assumption that the stochastic process is independent of the optimization decision. We address a class of problems where the optimization decisions ...

20150422BuchA Comment on "Computational Complexityof Stochastic Programming Problems" Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal workof Dyer and Stougie (Mathematical ...

20051229BuchA Comparative Study of Decomposition Algorithms for Stochastic Combinatorial Optimization This paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from three different applications. In order to preserve the commonalities among the algorithms in ...

20100604BuchA comparison of samplebased stochastic optimal control methods In this paper, we compare the performance of two scenariobased numerical methods to solve stochastic optimal control problems: scenario trees and particles. The problem consists in ﬁnding strategies to control a dynamicalsystem ...

20090519BuchA computational study of a solver system for processing twostage stochastic linear programming problems Formulation of stochastic optimisation problems and computational algorithms for their solution continue to make steady progress as can be seenfrom an analysis of many developments in this ﬁeld. The edited volume by Wallace ...

20101129BuchA computational study of a solver system forprocessing twostage stochastic linearprogramming problems

20000120BuchA Dynamic Asset Allocation Model with Downside Risk Control This paper presents a new stochastic model for investment. The investor's objective is to maximize the expected growth rate while controlling for downside risk. Assuming lognormally distributed prices, the strategy that ...

20080306BuchA dynamic dayahead paratransit planning problem We consider a dynamic planning problem for the transport of elderly and disabled people. The focus is on a decision to make one day ahead:which requests to serve with own vehicles, and which ones to assign to subcontractors, ...

20040219BuchA factor 1/2 approximation algorithm for a class of twostage stochastic mixedinteger programs Abstract We introduce the twostage stochastic maximumweight matching problem and demonstrate that this problem is NPcomplete. We give a factor 1/2 approximation algorithm and prove its correctness. We also provide a ...

20000704BuchA finite branch and bound algorithm for twostage stochastic integer programs This paper addresses a general class of twostage stochastic programs with integer recourse and discrete distributions. We exploit the structure of the value function of the second stage integer problem to develop a novel ...

20110913BuchA gradient formula for linear chance constraints under Gaussian distribution We provide an explicit gradient formula for linear chance constraints under a (possibly singular) multivariate Gaussian distribution. This formula allows one to reduce the calculus of gradients to the calculus of values ...

20010406BuchA heuristic for generating scenario trees for multistage decision problems In stochastic programming models we always face the problem of how to represent the random variables. This is particularly difficult with multidimensional distributions. We present an algorithm that produces a discrete ...

20061018BuchA JELS Stochastic inventory model with random demand A stochastic joint lot size model has been developed in which demand ofthe customer and the stock level of the vendor are assumed to be identicallydistributed continuous random variables. The effective ways for a ...

20130514BuchA mixedinteger stochastic nonlinear optimization problem with joint probabilistic constraints We illustrate the solution of a mixedinteger stochastic nonlinear optimization problem in an application of power management. In this application, a coupled system consisting of a hydro power station and a wind farm is ...

20090405BuchA model for dynamic chance constraints in hydro power reservoir management In this paper, a model for (joint) dynamic chance constraints is proposed and applied to an optimization problem in water reservoir management. The model relies on discretization of the decision variables but keeps the ...