Risk-averse stochastic optimal control: An efficiently computable statistical upper bound

被引：2

作者：

Guigues, Vincent ^{[1
]}

Shapiro, Alexander ^{[2
]}

Cheng, Yi ^{[2
]}

机构：

[1] FGV Praia Botafogo, Sch Appl Math, Rio De Janeiro, Brazil

[2] Georgia Inst Technol, Atlanta, GA 30332 USA

来源：

OPERATIONS RESEARCH LETTERS | 2023年 / 51卷 / 04期

关键词：

Stochastic programming; Stochastic optimal control; SDDP; Dynamic programming; Risk measures; Statistical upper bounds; PROGRAMS; DECOMPOSITION; CONVERGENCE;

D O I：

10.1016/j.orl.2023.05.002

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, we discuss an application of the Stochastic Dual Dynamic Programming (SDDP) type algorithm to nested risk-averse formulations of Stochastic Optimal Control (SOC) problems. We propose a construction of a statistical upper bound for the optimal value of risk-averse SOC problems. This outlines an approach to a solution of a long standing problem in that area of research. The bound holds for a large class of convex and monotone conditional risk mappings. Finally, we show the validity of the statistical upper bound to solve a real-life stochastic hydro-thermal planning problem. (c) 2023 Elsevier B.V. All rights reserved.

引用

页码：393 / 400

页数：8

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