A Complex Algorithm for Solving a Kind of Stochastic Programming

• 作者: Xinshun Ma
• 来源期刊: 应用数学与应用物理(英文)
• 学科: 数学
• 关键词: Stochastic Programming with Recourse Probability Distribution with Linear Partial Information Maximized Minimum Expectation Complex Algorithm
• 年，卷（期）: 2020,（6）
• 页码范围: 1016-1030
• 页数: 15页
• 分类号: O17

摘要

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.