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

Juan Torres(torres gueroa***at***lipn.univ-paris13.fr)
Can Li(canl1***at***andrew.cmu.edu)
Robert Apap(rapap***at***andrew.cmu.edu)
Ignacio Grossmann(grossmann***at***cmu.edu)

Abstract: This paper presents a tutorial on the state-of-the-art methodologies for the solution of two-stage (mixed-integer) linear stochastic programs and provides a list of software designed for this purpose. The methodologies are classifi ed 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.

Keywords: Stochastic programming  L-shaped method  Scenario Decomposition  Software benchmark

Category 1: Stochastic Programming

Category 2: Integer Programming ((Mixed) Integer Linear Programming )

Citation:

Download: [PDF]

Entry Submitted: 07/01/2019
Entry Accepted: 07/01/2019
Entry Last Modified: 07/01/2019

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