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Stochastic dynamic programming with random disturbances

Regina Hildenbrandt (2003)

Discussiones Mathematicae Probability and Statistics

Several peculiarities of stochastic dynamic programming problems where random vectors are observed before the decision ismade at each stage are discussed in the first part of this paper. Surrogate problems are given for such problems with distance properties (for instance, transportation problems) in the second part.

Stochastic geometric programming with an application

Jitka Dupačová (2010)

Kybernetika

In applications of geometric programming, some coefficients and/or exponents may not be precisely known. Stochastic geometric programming can be used to deal with such situations. In this paper, we shall indicate which stochastic programming approaches and which structural and distributional assumptions do not destroy the favorable structure of geometric programs. The already recognized possibilities are extended for a tracking model and stochastic sensitivity analysis is presented in the context...

Stochastic goal programming wth recourse

Antonio Heras Martínez, Ana García Aguado (1998)

Revista de la Real Academia de Ciencias Exactas Físicas y Naturales

In this article we discuss several alternative formulations for Stochastic Goal Programming. Only one of these models, which is a particular case of the Stochastic Programs with Recourse, is also compatible with Bayesian Decision Theory. Moreover, it is posible to approximate its solutions by means of an iterative algorithm.

Stochastic optimization problems with second order stochastic dominance constraints via Wasserstein metric

Vlasta Kaňková, Vadim Omelčenko (2018)

Kybernetika

Optimization problems with stochastic dominance constraints are helpful to many real-life applications. We can recall e. g., problems of portfolio selection or problems connected with energy production. The above mentioned constraints are very suitable because they guarantee a solution fulfilling partial order between utility functions in a given subsystem 𝒰 of the utility functions. Especially, considering 𝒰 : = 𝒰 1 (where 𝒰 1 is a system of non decreasing concave nonnegative utility functions) we obtain...

Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach

Alireza Amirteimoori, Saber Mehdizadeh, Sohrab Kordrostami (2022)

Kybernetika

In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct...

Strategies for LP-based solving a general class of scheduling problems.

Laureano F. Escudero, Gloria Pérez Sáinz de Rozas (1990)

Trabajos de Investigación Operativa

In this work we describe some strategies that have been proved to be very efficient for solving the following type of scheduling problems: Assume a set of jobs is to be performed along a planning horizon by selecting one from several alternatives for doing so. Besides selecting the alternative for each job, the target consists of choosing the periods at which each component of the work will be done, such that a set of scheduling and technological constraints is satisfied. The problem is formulated...

Strict convex regularizations, proximal points and augmented lagrangians

Carlos Humes Jr., Paulo Jose Da Silva E Silva (2010)

RAIRO - Operations Research

Proximal Point Methods (PPM) can be traced to the pioneer works of Moreau [16], Martinet [14, 15] and Rockafellar [19, 20] who used as regularization function the square of the Euclidean norm. In this work, we study PPM in the context of optimization and we derive a class of such methods which contains Rockafellar's result. We also present a less stringent criterion to the acceptance of an approximate solution to the subproblems that arise in the inner loops of PPM. Moreover, we introduce a new...

Strict minimizers of order m in nonsmooth optimization problems

Tadeusz Antczak, Krzysztof Kisiel (2006)

Commentationes Mathematicae Universitatis Carolinae

In the paper, some sufficient optimality conditions for strict minima of order m in constrained nonlinear mathematical programming problems involving (locally Lipschitz) ( F , ρ ) -convex functions of order m are presented. Furthermore, the concept of strict local minimizer of order m is also used to state various duality results in the sense of Mond-Weir and in the sense of Wolfe for such nondifferentiable optimization problems.

Strong average optimality criterion for continuous-time Markov decision processes

Qingda Wei, Xian Chen (2014)

Kybernetika

This paper deals with continuous-time Markov decision processes with the unbounded transition rates under the strong average cost criterion. The state and action spaces are Borel spaces, and the costs are allowed to be unbounded from above and from below. Under mild conditions, we first prove that the finite-horizon optimal value function is a solution to the optimality equation for the case of uncountable state spaces and unbounded transition rates, and that there exists an optimal deterministic...

Strong-weak Stackelberg Problems in Finite Dimensional Spaces

Aboussoror, Abdelmalek, Loridan, Pierre (1995)

Serdica Mathematical Journal

We are concerned with two-level optimization problems called strongweak Stackelberg problems, generalizing the class of Stackelberg problems in the strong and weak sense. In order to handle the fact that the considered two-level optimization problems may fail to have a solution under mild assumptions, we consider a regularization involving ε-approximate optimal solutions in the lower level problems. We prove the existence of optimal solutions for such regularized problems and present some approximation...

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