A Decision Support System with EDA_PR Algorithm for the Hot Rolling Scheduling

Article Preview

Abstract:

This paper presents a hybrid algorithm for the hot rolling scheduling problem, which is derived from the actual steel production, and some features make the solution methodology more difficult. The hybrid strategy is based on the solution construction mechanism of estimation of distribution algorithm (EDA) with path relinking (PR), an evolutionary method, which results in a novel approach that we call EDA_PR. Moreover, a decision support system in which the algorithm has been embedded for the hot rolling scheduling is designed. The computational experiments show that the EDA_PR method has more potential for improvement to solve the hot rolling scheduling problem compared with the manual scheduling method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

4466-4470

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] L. Lopez, M.W. Carter, and M. Gendreau, The hot strip mill production scheduling problem: a tabu search approach, European Journal of Operational Research 106 (1998) 317-335.

DOI: 10.1016/s0377-2217(97)00277-4

Google Scholar

[2] P. Cowling, Optimisation in steel hot rolling, Optimization in industry, Wiley, Chichester, England, 1995, PP. 55-66.

Google Scholar

[3] P. Cowling and W. Rezig, Integration of continuous caster and hot strip mill planning for steel production, Journal of Scheduling 3 (2000) 185-208.

DOI: 10.1002/1099-1425(200007/08)3:4<185::aid-jos42>3.0.co;2-g

Google Scholar

[4] E. Balas, The prize collecting traveling salesman problem, Networks 19 (1989) 621-636.

DOI: 10.1002/net.3230190602

Google Scholar

[5] L.X. Tang, J.Y. Liu, A.Y. Rong, and Z.H. Yang, Multiple traveling salesman problem model for hot scheduling in Shanghai Baoshan Iron & Steel Complex, European Journal of Operational Research 124 (2000) 267-282.

DOI: 10.1016/s0377-2217(99)00380-x

Google Scholar

[6] M. Pelikan, D.E. Goldberg, and F. Lobo, A survey of optimization by building and using probabilistic models, Computational Optimization and Applications 21 (2002) 5–20.

Google Scholar

[7] M. Hauschild, M. Pelikan, An introduction and survey of estimation of distribution algorithms, Swarm and Evolutionary Computation 1 (2011) 111–128.

DOI: 10.1016/j.swevo.2011.08.003

Google Scholar

[8] F. Glover, M. Laguna, and M. Marti, Fundamentals of Scatter Search and Path Relinking, Control and Cybernetics 39(3) (2000) 653–684.

Google Scholar

[9] S.C. Ho, M. Gendreau, Path relinking for the vehicle routing problem, Journal of Heuristics 12 (2006) 55–72.

DOI: 10.1007/s10732-006-4192-1

Google Scholar