A Complex Algorithm for Solving a Kind of Stochastic Programming

Autor:	Xinshun Ma, Yunpeng Luo
Rok vydání:	2020
Předmět:	Variable (computer science) Mathematical optimization Polyhedron Computer science Complete information Linear partial information Probability distribution Sample (statistics) Random variable Stochastic programming
Zdroj:	Journal of Applied Mathematics and Physics. :1016-1030
ISSN:	2327-4379 2327-4352
DOI:	10.4236/jamp.2020.86079
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d23e2e8d5f8c4e2dc42b3742c0fec3fa https://doi.org/10.4236/jamp.2020.86079 Zobrazit plný text záznamu