The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution

Abstract This article addresses a problem of minimizing the hot rolling time of an ingot, from a given initial thickness to a prescribed final one, subject to a number of system constraints. The idea is to determine the minimum possible odd number of passes, so that the ingot leaves in the same direction as it entered, which would ensure the necessary degree of reduction without violating the prescribed upper limits of the available torque and roll force. A maximum rolling velocity was also prescribed and additional restrictions were imposed on the rates of acceleration and deceleration inside the mill. The problem was solved by using a number of variants of genetic algorithms, including a multipopulation island model and differential evolution, besides the simple genetic algorithms. The results are compared with some earlier work based on a discrete dynamic programming technique, and a model based on an improved formulation is also presented.

[1]  Nirupam Chakraborti,et al.  Tight-binding calculations of Si-H clusters using genetic algorithms and related techniques: Studies using differential evolution , 2001 .

[2]  Nirupam Chakraborti,et al.  A study of the continuous casting mold using a pareto-converging genetic algorithm , 2001 .

[3]  Nirupam Chakraborti,et al.  A genetic algorithm based heat transfer analysis of a bloom re-heating furnace , 2000 .

[4]  A. K. Tieu,et al.  Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills , 2000 .

[5]  Lixin Tang,et al.  A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex , 2000, Eur. J. Oper. Res..

[6]  Nirupam Chakraborti,et al.  Optimisation of continuous casting mould parameters using genetic algorithms and other allied techniques , 2000 .

[7]  Mitsuo Gen,et al.  A solution method for optimal cost problem of welded beam by using genetic algorithms , 1999 .

[8]  J. Chung,et al.  Application of a genetic algorithm to process optimal design in non-isothermal metal forming , 1998 .

[9]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[10]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[11]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[12]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  W. Harmon Ray,et al.  Process optimization, with applications in metallurgy and chemical engineering , 1973 .