Learning-based multi-objective evolutionary algorithm for batching decision problem

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.

Automated summary

This study investigates a multi-objective batching decision problem in batch annealing operations in the iron and steel industry. A data-driven method is developed to determine technological parameters for a multi-objective optimization model, and a learning-based multi-objective evolutionary algorithm (LBMOEA) with novel operators is proposed. Computational experiments show that LBMOEA with clustering method is effective and efficient, demonstrating potential for practical production applications.