A differential evolution algorithm with double-mode crossover for supply chain scheduling in cold rolling

This paper studies a supply chain scheduling problem in cold rolling, which is derived from practical steel production. The problem is to make coil schedules for all the production lines involved in the supply chain, with the aim of balancing the capacity of each production line, and minimizing the total changeover cost. To describe the problem, we formulate a mixed integer linear programming (MILP) model with consideration of all practical technological requirements. The strong AP-hardness of the problem motivates us to develop an improved discrete differential evolution (DE) algorithm to solve it. We represent individuals of the population as integer-coded matrixes. In the proposed DE algorithm, an improved mutation operation and a new double-mode crossover operation which is composed of two operators with different evolution purposes are proposed. Moreover, self-adaptive control parameters cooperated with the proposed evolution strategies are adopted to enhance the performance of algorithm. By computational experiments, the results show that the proposed DE algorithm outperforms the compared DE algorithms for solving the supply chain scheduling problem. In addition, the proposed algorithm is also competitive in comparison with the commercial optimization solver CPLEX.