An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals

With the development of intelligent manufacturing, production scheduling and preventive maintenance are widely applied in industry to enhance production efficiency and machine reliability. Therefore, according to the different processing states and the physical degradation phenomena of the machine, this paper proposes an accurate maintenance (AM) model based on reliability intervals, which have different maintenance activities in diverse intervals and overcome the shortcoming of the single reliability threshold maintenance model used in the past. Combining the flexible job-shop scheduling problem (FJSP), an integrated multiobjective optimization model is established with production scheduling and accurate maintenance. To strengthen the ability of the evolutionary algorithm to solve the presented model/problem, we propose a novel genetic algorithm, named the approximate nondominated sorting genetic algorithm III (ANSGA-III), which is inspired by NSGA-III. To improve the performance of the Pareto dominance principle, the local search, the elite storage for the original algorithm, the approximate dominance principle, the variable neighborhood search, and the elite preservation strategy are proposed. Then, we employ a scheduling example to verify and evaluate the availability of the above three improved operations and the proposed algorithm. Next, we compare ANSGA-III against five recently proposed algorithms, representing the state-of-the-art on similar problems. Finally, we apply ANSGA-III to solve the integrated optimization model, and the results reveal that the machine can maintain higher availability and reliability when compared to other models in our experiments. Consequently, the superiority of the proposed model based on accurate maintenance of reliability intervals is demonstrated, and the optimal reliability threshold between the yellow and red areas is found to be 0.82.