A multi-objective optimisation algorithm for the hot rolling batch scheduling problem

The hot rolling batch scheduling problem is a hard problem in the steel industry. In this paper, the problem is formulated as a multi-objective prize collecting vehicle routing problem (PCVRP) model. In order to avoid the selection of weight coefficients encountered in single objective optimisation, a multi-objective optimisation algorithm based on Pareto-dominance is used to solve this model. Firstly, the Pareto ℳ𝒜𝒳–ℳℐ𝒩 Ant System (P-ℳℳAS), which is a brand new multi-objective ant colony optimisation algorithm, is proposed to minimise the penalties caused by jumps between adjacent slabs, and simultaneously maximise the prizes collected. Then a multi-objective decision-making approach based on TOPSIS is used to select a final rolling batch from the Pareto-optimal solutions provided by P-ℳℳAS. The experimental results using practical production data from Shanghai Baoshan Iron & Steel Co., Ltd. have indicated that the proposed model and algorithm are effective and efficient.

[1]  Byung-In Kim.  Some comments on Chen et al. ‘Production scheduling optimization algorithm for the hot rolling processes’ , 2010 .

[2]  Peter Cowling,et al.  A flexible decision support system for steel hot rolling mill scheduling , 2003, Comput. Ind. Eng..

[3]  Changchun Pan,et al.  A method of solving a large-scale rolling batch scheduling problem in steel production using a variant of column generation , 2009, Comput. Ind. Eng..

[4]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[5]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Shixin Liu.  Model and Algorithm for Hot Rolling Batch Planning in Steel Plants , 2010 .

[7]  Genke Yang,et al.  Production scheduling optimization algorithm for the hot rolling processes , 2008 .

[8]  Richard F. Hartl,et al.  Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection , 2004, Ann. Oper. Res..

[9]  Jeff R. Wright,et al.  Discrete event sequencing as a Traveling Salesman Problem , 1992 .

[10]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[11]  Yu-Wang Chen,et al.  Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling , 2008 .

[12]  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..

[13]  Benjamín Barán,et al.  A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows , 2003, Applied Informatics.

[14]  Jun Zhao,et al.  A two-stage scheduling method for hot rolling and its application , 2009 .

[15]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[16]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[17]  Michel Gendreau,et al.  The hot strip mill production scheduling problem: A tabu search approach , 1998, Eur. J. Oper. Res..

[18]  Luca Maria Gambardella,et al.  MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows , 1999 .

[19]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[20]  Xiong Chen,et al.  Modeling rolling batch planning as vehicle routing problem with time windows , 1998, Comput. Oper. Res..

[21]  Lixin Tang,et al.  Iterated local search algorithm based on very large-scale neighborhood for prize-collecting vehicle routing problem , 2006 .

[22]  Francisco Herrera,et al.  A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP , 2007, Eur. J. Oper. Res..