Title :
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
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;
Conference_Titel :
Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-8727-1
DOI :
10.1109/CSAE.2011.5952829