GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem - Luo, Jia; Fujimura, Shigeru; El Baz, Didier; Plazolles, Bastien (Journal of parallel and distributed computing, 2019)
Due to new government legislation, customers’ environmental concerns and continuously rising cost of energy, energy efficiency is becoming an essential parameter of industrial manufacturing processes in recent years. Most efforts considering energy issues in scheduling problems have focused on static scheduling. But in fact, scheduling problems are dynamic in the real world with uncertain new arrival jobs after the execution time. This paper proposes an energy efficient dynamic flexible flow shop scheduling model using the peak power value with consideration of new arrival jobs. As the problem is strongly NP-hard, a priority based hybrid parallel Genetic Algorithm with a predictive reactive complete rescheduling strategy is developed. In order to achieve a speedup to meet the short response in the dynamic environment, the proposed method is designed to be highly consistent with the NVIDIA CUDA software model. Finally, numerical experiments are conducted and show that our approach can not only solve the problem flexibly, but also gain competitive results and reduce time requirements dramatically. •An energy efficient dynamic flexible flow shop scheduling (EDFFS) model is studied.•A parallel Genetic Algorithm (GA) with a predictive reactive complete rescheduling approach is developed.•The parallel GA is highly consistent with the hierarchy of threads and memory of CUDA.•The parallel GA obtains competitive results and reduces time requirements dramatically.•The proposed method is flexible to solve the EDFFS problem and overcomes the traditional static approach.