Mohammad Niyayesh

• Steel manufacturing is characterized by its high energy consumption and the production of high-value-added products. In real-world steel production, unforeseen events frequently disrupt schedules, emphasizing the critical need for effective and adaptive planning to ensure continuous operations. This study makes significant contributions to the steel industry by offering new approaches to improve efficiency, streamline operations, and optimize production processes, ultimately driving advancements in steel manufacturing performance. To address the challenges inherent to EAF-based steelmaking, a seamless pipeline of algorithms has been developed. This pipeline works together to enhance the manufacturing planning process by providing an accurate chemical condensation of molten steel and classifying this outcome according to the most feasible steel grade, which can be obtained with a minimum of purification. Finally, the pipeline reschedules the planning process with the objective of maximizing machine utilization. To achieve these goals, the steelmaking stage key performance indicators (KPIs) that have the most impact on the quality of the final product were first identified. In the next step, a novel prediction algorithm utilizing a multilayer feedforward neural network was developed to estimate key quality parameters. Finally, to enhance the pipeline's resilience to disruptions, a Genetic Algorithm (GA) is employed to mitigate the impact of scenarios where processing times for jobs vary and do not align with the established schedule. This is a prevalent disruption event that renders the base schedule infeasible. The proposed algorithm aims to minimize total completion and waiting times, thereby enhancing operational efficiency and minimizing manufacturing costs.