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

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 variab...

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Bibliographic Details
Published in:Algorithms Vol. 15; no. 4; p. 103
Main Authors: Torres, Juan J., Li, Can, Apap, Robert M., Grossmann, Ignacio E.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2022
Subjects:
Algorithms
Benders decomposition
Decomposition
Expected values
L-shaped method
Linear programming
Mixed integer
Optimization
scenario decomposition
Software
software benchmark
Stochastic programming
Systems engineering
ISSN:1999-4893, 1999-4893
Online Access:Get full text
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Summary: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.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a15040103