Modelling and discrete differential evolution algorithm for order rescheduling problem in steel industry

Order management is a critical and complicated issue in the production process of iron and steel industry, since orders are the bridge between customers and semi-finished/final products in different units. Usually, the scheduling of orders is arranged by skilled planners. However, the initial scheduling may be infeasible during the production process due to the dynamic and frequent variation of production environment. This paper investigates a practical order rescheduling problem to adapt various changes that affect the normal production. The problem is formulated as a mixed integer programming mathematical model considering the original objective, the deviation from the initial scheduling and the equilibrium of production capacity. A discrete differential evolution algorithm with new mutation and crossover operators is proposed to find near-optimal solutions of this problem. Computational experiments are presented on both randomly generated instances and the instances from the practical production. Experimental results illustrate that the proposed algorithm could obtain better solutions compared with four standard differential evolution algorithms and the manual method. Furthermore, a production data based practical decision support system embedding the model and algorithm is developed to monitor the production process, diagnose whether there are high-impact changes of the orders and units, and make rescheduling decisions if necessary.