A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software

Autor:	Juan J. Torres, Can Li, Robert M. Apap, Ignacio E. Grossmann
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	stochastic programming L-shaped method scenario decomposition software benchmark Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Algorithms, Vol 15, Iss 4, p 103 (2022)
Druh dokumentu:	article
ISSN:	1999-4893
DOI:	10.3390/a15040103
Popis:	This paper presents a tutorial on the state-of-the-art software for the solution of two-stage (mixed-integer) linear stochastic programs and provides a list of software designed for this purpose. The methodologies are classified according to the decomposition alternatives and the types of the variables in the problem. We review the fundamentals of Benders decomposition, dual decomposition and progressive hedging, as well as possible improvements and variants. We also present extensive numerical results to underline the properties and performance of each algorithm using software implementations, including DECIS, FORTSP, PySP, and DSP. Finally, we discuss the strengths and weaknesses of each methodology and propose future research directions.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5ff2115ffef14409a8f7928db3bba217 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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