随机对偶动态规划情景格构造方法的实验研究

D. Golembiovsky, A. Pavlov, S. Daniil
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引用次数: 0

摘要

随机对偶动态规划(SDDP)算法的应用越来越多。在本文中,我们分析了SDDP的不同格构方法,举例说明了报贩问题的一个现实变体,包括生产的存储。我们对几天的工作进行了建模,并比较了使用不同的网格构建方法实现的利润以及在网格构建中花费的相应计算机时间。我们的情况与已知的情况不同,因为我们不仅考虑多维情况,而且考虑具有阶段依赖性的多阶段情况。我们为不同的马尔可夫过程构造了场景格,这些过程在随机建模中起着至关重要的作用。我们工作的新颖之处在于比较不同的场景格构建方法。我们考虑了报刊供应商问题的一个现实变体。本文给出的结果表明,Voronoi方法略优于其他方法,但总体而言,k-means方法要快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimental Study of Methods of Scenario Lattice Construction for Stochastic Dual Dynamic Programming
The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.
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