A Heuristic Mutation Based Genetic Algorithm for Fast Parallel Scheduling of Steel Cold Rolling

• Formulates a cold rolling scheduling problem as a mixed integer linear program (MILP) with an economic objective, incorporating practical constraints like due dates, batch attributions, and parallel heterogeneous machines. • Proposes a modified genetic algorithm (GA) with heuristic initialization, three heuristic mutation operators, and parallel computing to efficiently solve the MILP. • Simulation results confirm the method's effectiveness in generating optimized scheduling schemes for cold rolling, improving operational efficiency and profitability. • Addresses unique challenges in steel cold rolling, including batch size preferences and job compatibility constraints, which are often overlooked in existing literature.