Journal ArticleDOI
State-of-the-Art-Survey—Stochastic Programming: Computation and Applications
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The basic methodology for optimal-decision models for stochastic programming models, recent developments in computation, and several practical applications are described.Abstract:
Although decisions frequently have uncertain consequences, optimal-decision models often replace those uncertainties with averages or best estimates. Limited computational capability may have motivated this practice in the past. Recent computational advances have, however, greatly expanded the range of optimal-decision models with explicit consideration of uncertainties. This article describes the basic methodology for these stochastic programming models, recent developments in computation, and several practical applications.read more
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Optimization under uncertainty: state-of-the-art and opportunities
TL;DR: This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty and discusses and contrast the classical recourse-based stochastic programming, robust stochastics programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastically dynamic programming.
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Retrospective on optimization
TL;DR: A general classification of mathematical optimization problems is provided, followed by a matrix of applications that shows the areas in which these problems have been typically applied in process systems engineering.
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Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis
TL;DR: The interplay between objective and constraints in a number of single-period variants, including semivariance models are described, revealing the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.
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A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System
TL;DR: In this paper, a stochastic cost model and a solution technique for optimal scheduling of the generators in a wind integrated power system considering the demand and wind generation uncertainties are presented for optimal day-ahead planning even with indeterminate information about the wind generation.
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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.