作者: Monocle 時間: 2025-3-21 22:39
Book 2024tation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book’s most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example 作者: arbiter 時間: 2025-3-22 01:19
https://doi.org/10.1007/978-94-6300-902-7also use it in later chapters of the book. In this chapter, we begin with illustrations of deterministic models applied to the numerical example and then move on to risk-neutral stochastic models. We end the chapter with illustrations of risk-averse models introduced in the previous chapter.作者: OASIS 時間: 2025-3-22 05:52
Junyi Zhang,Wonchul Kim,Akimasa Fujiwaratrices, we provide a review of sparse matrix formats in Sect. 10.3. We discuss program design for algorithm implementation and testing in Sect. 10.4 and end the chapter with a review of empirical analysis, methods of analysis, test problems, and reporting computational results in Sect. 10.5.作者: ellagic-acid 時間: 2025-3-22 10:47
Modeling and Illustrative Numerical Examplesalso use it in later chapters of the book. In this chapter, we begin with illustrations of deterministic models applied to the numerical example and then move on to risk-neutral stochastic models. We end the chapter with illustrations of risk-averse models introduced in the previous chapter.作者: 導(dǎo)師 時間: 2025-3-22 13:20 作者: 導(dǎo)師 時間: 2025-3-22 17:45 作者: 阻塞 時間: 2025-3-22 22:42
Sampling-Based Stochastic Linear Programming Methodsin which sequential sampling is done to solve the approximation problem. We illustrate interior sampling with the basic stochastic decomposition (SD) method for MR-SLP. Since we place emphasis on algorithm computer implementation, we also discuss how to generate random samples from the instance data.作者: 極小 時間: 2025-3-23 05:02 作者: Melodrama 時間: 2025-3-23 09:06
Paul Emeka Okeke,Isunueo Benedicta Omeghien different classes of SP, i.e., stochastic linear programming (SLP), stochastic mixed-integer programming (SMIP), and probabilistically constrained stochastic programming (PC-SP). We provide simplified problem formulations with a focus on how to model the key elements of the problem.作者: 沒花的是打擾 時間: 2025-3-23 12:43 作者: 無王時期, 時間: 2025-3-23 17:40 作者: 悅耳 時間: 2025-3-23 20:51
https://doi.org/10.1007/978-3-031-52464-6Mean-risk linear and integer models; Risk Measures; Risk-Averse Models; computational experimentation; c作者: 埋葬 時間: 2025-3-24 00:44
Springer Nature Switzerland AG 2024作者: demote 時間: 2025-3-24 02:53 作者: Invigorate 時間: 2025-3-24 08:47 作者: Impugn 時間: 2025-3-24 14:14 作者: entail 時間: 2025-3-24 16:13 作者: invade 時間: 2025-3-24 20:36 作者: 小溪 時間: 2025-3-25 01:37
https://doi.org/10.1007/978-3-642-23550-4f the models derived in Chap. . and decomposition techniques from Chap. . to derive solution algorithms for RN-SLP. We begin our study with the classical . in Sect. 6.2, which generates a single optimality cut at a given iteration of the algorithm to approximate the recourse function. We then consid作者: Hirsutism 時間: 2025-3-25 04:28
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作者: 不真 時間: 2025-3-25 09:43
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作者: 隼鷹 時間: 2025-3-25 13:54
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作者: 不可比擬 時間: 2025-3-25 16:33 作者: 古代 時間: 2025-3-25 23:22 作者: 我要威脅 時間: 2025-3-26 04:08
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 作者: Champion 時間: 2025-3-26 05:22
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作者: Cleave 時間: 2025-3-26 12:21 作者: Cerebrovascular 時間: 2025-3-26 15:56
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作者: minaret 時間: 2025-3-26 17:23
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 . 作者: 微粒 時間: 2025-3-26 23:21
Risk-Neutral Stochastic Linear Programming Methodsf the models derived in Chap. . and decomposition techniques from Chap. . to derive solution algorithms for RN-SLP. We begin our study with the classical . in Sect. 6.2, which generates a single optimality cut at a given iteration of the algorithm to approximate the recourse function. We then consid作者: 吸引力 時間: 2025-3-27 04:40
Mean-Risk Stochastic Linear Programming Methods 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作者: atopic-rhinitis 時間: 2025-3-27 07:42
Sampling-Based Stochastic Linear Programming Methodsochastic 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作者: incredulity 時間: 2025-3-27 11:41
Stochastic Mixed-Integer Programming Methodso 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作者: 歡呼 時間: 2025-3-27 15:04
Computational Experimentationdition to theory, models, and algorithms, implementation and application of the models and algorithms is also important. Implementing (coding) the models and algorithms on the computer requires computational experimentation. Therefore, it is fitting to end this book with a chapter on computational e作者: 抵押貸款 時間: 2025-3-27 21:09 作者: pacific 時間: 2025-3-28 01:19 作者: Cardiac-Output 時間: 2025-3-28 04:23
Andrea Caccialanza,Marco Marinonierefore, as in Kelley’s method, Benders decomposition algorithm generates cutting-planes (row generation). For problems in high-dimensional space, we introduce . to potentially reduce the number of iterations in Benders decomposition algorithm. In this version of the algorithm, we add a quadratic te作者: 補助 時間: 2025-3-28 09:38
https://doi.org/10.1007/978-3-642-23550-4ithms for RN-SLP may not be an easy activity for many students. Therefore, in our derivation of the various algorithms, we place emphasis on implementation and provide guidelines for efficient computer codes. We end the chapter with a list of other decomposition methods for RN-SLP not covered in thi作者: Ascribe 時間: 2025-3-28 11:14 作者: 法律 時間: 2025-3-28 17:23 作者: 一條卷發(fā) 時間: 2025-3-28 20:23 作者: oncologist 時間: 2025-3-29 02:16 作者: recede 時間: 2025-3-29 03:45
Introductiontochastic setting of SP. We assume that the reader is familiar with LP and skip all the fundamental concepts of LP such as duality theory and sensitivity analysis. Understanding these LP concepts is important to studying SP. We first introduce scenario trees for representing the underlying stochasti作者: GRAZE 時間: 2025-3-29 08:51 作者: Flustered 時間: 2025-3-29 12:23 作者: 有效 時間: 2025-3-29 18:23 作者: 燕麥 時間: 2025-3-29 22:33
Mean-Risk Stochastic Linear Programming Methodspter, we turn to the derivation of two subgradient-based algorithms for the deviation risk measure . (ASD), termed . and . algorithms. Unlike MR-SLP with QDEV, CVaR, and EE, the DEP for ASD has a block angular structure due to a set of linking constraints. Therefore, the L-shaped method is . applica
