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Learning-based multi-objective evolutionary algorithm for batching decision problem
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AI
DOI：10.1016/j.cor.2022.106026.png)
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Abstract
En 中文
This study investigates a multi-objective batching decision problem that arises in batch annealing operations in the iron and steel industry. The problem concerns selecting coils from a set of waiting coils to be annealed to form batches so as to maximize two conflicting objectives: product quality and equipment utilization. In this study, a multi-objective optimization model is formulated, and a data-driven method is developed to determine the technological parameters for the model. To improve the efficiency and effectiveness of the solution process, we propose a learning-based multi-objective evolutionary algorithm (LBMOEA) with novel evolution operators and a learning-based solution space reduction strategy. To more quickly solve the problem, a clustering method is adopted to achieve a parallel mechanism in the LBMOEA. In computational experiments on 20 randomly generated instances, the results demonstrate that the above evolution operators and strategy are effective, and that the clustering method can reduce the average time cost by 58.76%. For 15 practical production instances, the results illustrate that the LBMOEA with the clustering method is superior to the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D), and shows good potential for application in practical production.
Keywords:
Batching decision
Steel production
Multi-objective optimization
Evolutionary algorithm
Learning method
Journal
C
IF:
4.3
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6.5K
Citations:
1.8W