A Q-learning-based evolutionary algorithm for solving the low-carbon multi-objective flexible job shop scheduling problem

In recent years, how to reduce energy consumption at the manufacturing system level in the low-carbon multi-objective flexible job shop scheduling problem (LCM-FJSP) has received significant attention. In this research, a model with the maximum completion time, total machine workload and total carbon emissions is built. Moreover, a Q-learning-based adaptive weight-adjusted decomposition evolutionary algorithm (QMOEA/D-AWA) is proposed. In the QMOEA/D-AWA, an initialization strategy with four heuristic initial rules for obtaining high-quality population, a variable neighborhood search strategy with four problem-specific local search methods for enhancing exploration and a Q-learning-based parameter adaptive strategy for automatically determining the number of neighborhood solutions are designed. To validate the effectiveness of the proposed QMOEA/D-AWA, it is compared with five state-of-the-art algorithms on 15 instances. In the statistical analysis, the QMOEA/D-AWA obtains the overwhelming metric results in 10 instances. In the visual analysis, the completion time is reduced by 3.74%, the total workload is reduced by 3.94%, and the carbon emissions are reduced by 5.94%.