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,...

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Bibliographic Details
Published in:International journal of production research Vol. 51; no. 3; pp. 667 - 681
Main Authors: Jia, S.J., Yi, J., Yang, G.K., Du, B., Zhu, J.
Format: Journal Article
Language:English
Published: London Taylor & Francis Group 01.02.2013
Taylor & Francis LLC
Subjects:
Algorithms
ant colony optimisation
Decision making models
Effectiveness studies
Hot rolling
hot rolling batch scheduling
Iron and steel industry
Mathematical models
Mathematical problems
multi-objective optimisation
Optimization
Optimization algorithms
Pareto optimality
Pareto optimisation
Pareto optimum
Production scheduling
Scheduling
Structural steels
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary: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 ℳ -ℳℐ 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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.654138