Production scheduling optimization algorithm for the hot rolling processes

The hot rolling production scheduling problem is an extremely difficult and time-consuming process, so it is quite difficult to achieve an optimal solution with traditional optimization methods owing to the high computational complexity. To ensure the feasibility of solutions and improve the efficiency of the scheduling, this paper proposes a vehicle routing problem to model the problem and develops an easily implemented hybrid approach to solve the problem. In the hybrid approach, quantum particle swarm optimization combines local search and global search to search the optimal results and simulated annealing employs certain probability to avoid getting into a local optimum. The computational results from actual production data have shown that the proposed model and algorithm are feasible and effective for the hot rolling scheduling problem.