Detail publikace

# Decomposition Approach Applied to Stochastic Optimization of Continuous Steel Casting

Originální název

Decomposition Approach Applied to Stochastic Optimization of Continuous Steel Casting

Anglický název

Decomposition Approach Applied to Stochastic Optimization of Continuous Steel Casting

Jazyk

en

Originální abstrakt

The purpose of the paper is to present an original stochastic programming approach based on the implementation of the decomposition algorithm for continuous casting problem. The uncertain parameters are modeled by random elements with discerete probability distributions. Therefore, the suitable model is a scenario-based stochastic program with two stages. Among the docomposition algorithms, we have chosen the progressive hedging algorithm (PHA) that is suitable for the case when nonlinear programs are related to scenarios. The example base on the real-world data is computed and results are discussed.

Anglický abstrakt

The purpose of the paper is to present an original stochastic programming approach based on the implementation of the decomposition algorithm for continuous casting problem. The uncertain parameters are modeled by random elements with discerete probability distributions. Therefore, the suitable model is a scenario-based stochastic program with two stages. Among the docomposition algorithms, we have chosen the progressive hedging algorithm (PHA) that is suitable for the case when nonlinear programs are related to scenarios. The example base on the real-world data is computed and results are discussed.

Dokumenty

BibTex

``````
@inproceedings{BUT36996,
author="Lubomír {Klimeš} and Pavel {Popela} and Josef {Štětina}",
title="Decomposition Approach Applied to Stochastic Optimization of Continuous Steel Casting",
annote="The purpose of the paper is to present an original stochastic programming approach based on the implementation of the decomposition algorithm for continuous casting problem. The uncertain parameters are modeled by random elements with discerete probability distributions. Therefore, the suitable model is a scenario-based stochastic program with two stages. Among the docomposition algorithms, we have chosen the progressive hedging algorithm (PHA) that is suitable for the case when nonlinear programs are related to scenarios. The example base on the real-world data is computed and results are discussed.",