Stochastic Programming

From the contents: Theoretical Models: Types of Asymptotic Approximations for Normal Probability Integrals.- Strong Convexity and Directional Derivatives of Marginal Values in Two-Stage Stochastic Programming.- Numerical Methods and Computer Support: Computation of Probability Functions and its Dervatives by means of Orthogonal Function Series Expansions.- Computer Support for Modeling in Stochastic Linear Programming.- Structural Design via Evolution Strategies.- On the Regularized Decomposition Method for Stochastic Programming Problems.- Multipoint Approximation Method for Structural Optimization Problems with Noisy Function Values.- Sequential Convex Programming Methods.- Engineering Applications: Target Costing: The Data Problem.- Statistical Characterization of Granular Assemblies.- On Stochastic Aspects of a Metal Cutting Problem.- Consideration of Stochastic Effects for Finding Optimal Layouts of Mechanical Structures.- Tolerance Dynamics for a High Precision Balance.- On Boundary-Initial Value Problem for Linear Hyperbolic Thermoelasticity Equations with Control of Temperature.