A hybrid algorithm for integrated scheduling problem of complex products with tree structure

Due to the poor design of encoding methods or evolutionary operators in previous genetic-algorithm-based integrated scheduling algorithms, this paper proposes an integrated scheduling algorithm based on a hybrid genetic algorithm and tabu search. Firstly, an encoding method based on a dynamic schedulable operation set is proposed. This method cannot only reflect the priority constraints among operations, but also avoid the problems of imposing constraints and missing solution space in previous division encoding method. Secondly, a decoding method based on machine idle time driving is presented to handle the scheduling order of operations on different machines. Then, two different discrete crossover and mutation operators are designed to ensure the legitimacy of the processing sequence of the same machine. Finally, a local search strategy based on tabu search is shown to enhance the search capability for superior solutions. The algorithm is tested by the randomly generated instances, and the experimental results indicate that the proposed algorithm is effective and can achieve satisfactory performance.