Titlebook: Computational Stochastic Programming; Models, Algorithms, Lewis Ntaimo Book 2024 Springer Nature Switzerland AG 2024 Mean-risk linear and

Hirsutism

Christine Behnke,Bertram Meimbresse derived in Chap. 2 and decomposition techniques from Chap. 6 to derive solution algorithms for MR-SLP for quantile and deviation risk measures. Definitions of risk measures and deterministic equivalent problem (DEP) formulations are derived in Chap. 2. The risk measures . (QDEV), . (CVaR), and . EE

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Philip Michalk,Bertram Meimbresseochastic programming (SP) models derived in Chap. . and decomposition techniques from Chaps. . and . in the solution methods for MR-SLP. We study two main classical approaches, . and .. Exterior sampling or Monte Carlo methods involve taking a sample and solving an approximation problem, and getting

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https://doi.org/10.1007/978-3-642-23550-4o the stochastic setting. Thus, SMIP inherits the nonconvexity properties of MIP and with its large-scale nature due to data uncertainty, SMIP is very challenging to solve. Therefore, it is not surprising that there are few practical algorithms for SMIP. This motivates the study of SMIP due to its m

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Introductionth optimization problems involving data uncertainties and risk. We begin with the motivation and explain why SP has become so pervasive in operations research, science, and engineering and discuss some of its diverse set of example applications that span our everyday lives. In Sect. 1.2, we provide

Champion

Stochastic Programming Models in many decision-making problems in operations research and engineering involving risk. We introduce risk functions in Sect. 2.1 and the notion of risk measures, describing axioms that define a coherent risk measure. We consider two main classes of stochastic programming: mean-risk stochastic progr

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Example Applications of Stochastic Programmingtion planning, facility location, supply chain planning, fuel treatment planning, healthcare appointment scheduling, airport time slot allocation, air traffic flow management, satellite constellation scheduling, wildfire response planning, and vaccine allocation for epidemics. These applications spa

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Deterministic Large-Scale Decomposition Methods the foundation for decomposition methods for stochastic programming that followed, starting with the classical L-shaped method of Van Slyke and Wets in 1969. We begin our study with . for optimizing a convex function over a convex compact set using cutting-planes in Sect. 5.2. We then move on to .