Abstract
Pursuing the intellectualization of a steelmaking plant and developing a charge plan of the steelmaking-continuous casting section are critical in metallurgy engineering. Herein, we aim to develop a charge plan model based on the operation of the steelmaking-continuous casting section to minimize the penalty values of residual materials; of a contract not selected and the penalty values that is caused by the difference in steel grades, the width and the delivery time between slabs in the same charge. We introduce an improved elitist genetic algorithm (IEGA), define the matching chromosome coding and decoding strategies, and suggest improving the selection, crossover, and mutation operators. Finally, we verify the proposed model and algorithm on the production data of a real enterprise. We clarify the applicability of developing a charge plan based on model analysis and demonstrate the effectiveness of the IEGA through algorithm analysis.
Highlights
A batch plan for a steelmaking-continuous casting section (SM-CC) primarily comprises of a charge plan and a cast plan and is located in a transition layer between a contract plan and an operation plan
We thoroughly investigated the development of a charge plan model of SM-CC, formulated a suitable model, improved the conventional genetic algorithm, and successfully solved the problem based on the specific situation of the considered model
According to the characteristics of the model, we introduced an improved elitist genetic algorithm (IEGA) based on the permutation encoding method and proposed the corresponding chromosome decoding strategy
Summary
A batch plan for a steelmaking-continuous casting section (SM-CC) primarily comprises of a charge plan and a cast plan and is located in a transition layer between a contract plan and an operation plan It should rely on a production contract pool after preparing the contract plan and consider the slab width, production process, delivery time, and other constraints to realize continuous production at a steel plant. The charge plan optimization problem of a steelmaking process was formulated as an integer programming model and solved using the Tabu search algorithm. Huang et al [5] formulated a mathematical model to minimize residual materials and the steel-grade replacement costs They identified the weight factors of different optimization objectives to promote on-site decision-making and solved the problem using a dynamic programming algorithm. Considering two flows of continuous machine casting and Metals 2020, 10, 1196; doi:10.3390/met10091196 www.mdpi.com/journal/metals
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