A hybrid gene algorithm for byproduct blast furnace gas scheduling in iron and steel production

Author

Shi, Cantao ; Wang, Ningning ; Li, Tieke

Author_Institution

Sch. of Econ. & Manage., Univ. of Sci. & Technol. Beijing, Beijing, China

Volume

4

fYear

2011

fDate

10-12 June 2011

Firstpage

181

Lastpage

184

Abstract

This paper concentrates on the byproduct blast furnace gas scheduling (BFGS) problem in iron and steel enterprises. The units in a typical BFG system are categorized into four types, whose features are discussed. Then a MILP mathematical model is built to minimize the excess and the shortage of gas distribution. A hybrid algorithm combined gene algorithm (GA) and linear programming (LP) is presented based on benders decomposition. The computational tests show that the proposed hybrid algorithm is practically effective.

Keywords

blast furnaces; genetic algorithms; goods distribution; integer programming; linear programming; scheduling; steel manufacture; MILP mathematical model; benders decomposition; byproduct blast furnace gas scheduling problem; gas distribution shortage; hybrid gene algorithm; iron production; linear programming; steel production; Biological cells; Blast furnaces; Gases; Iron; Optimization; Production; Steel; blast furnace gas; byproduct gas schedule; hybrid gene algorithm; iron and steel enterprise;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-8727-1

Type

conf

DOI

10.1109/CSAE.2011.5952829

Filename

5952829