For the parallel machine scheduling with controllable processing times(PMS-CPT), this paper proposed a scheduling problem for optimizing tardiness and energy cost. First, this article developed an integer linear programming(ILP) model for the proposed scheduling problem in order to facilitate the CPLEX solver. Second, it established a hybrid teaching-learning-based optimization(HTLBO) algorithm to quickly obtain satisfactory solutions. This algorithm adopted a new coding and decoding method according to the nature of the proposed parallel machine scheduling problem, which overcame the weakness of standard teaching-learning-based optimization algorithm that could not be directly applied to discrete problems. Meanwhile, it also proposed a local optimizer based on variable neighborhood search(VNS), which aimed to enhance the performance of the hybrid algorithm. Finally, it conducted the simulations to solve instances of PMS-CPT. The experimental results demonstrate the feasibility and validity of the proposed ILP model and hybrid algorithm.

[2019-03-05] Printed Article /doc/2017.09.0929/v2