A knowledge-guided evolutionary algorithm incorporating reinforcement learning for energy efficient dynamic flexible job shop scheduling problem with machine breakdowns

Abstract

The flexible job shop scheduling problem is gradually developing towards greening and intelligence. However, in the real production, there are often various dynamic disturbances that result in lower executability of scheduling solutions. Therefore, this paper first investigates the energy efficient dynamic flexible job shop scheduling problem with machine breakdowns. To solve this problem, a knowledge-guided evolutionary algorithm incorporating reinforcement learning (KEARL) is established to minimize maximum completion time, total energy consumption, and workload of critical machines, which is a mixed-integer linear programming model with transportation time of jobs and setup time of machines included. In KEARL, a new rescheduling strategy is designed to reduce the possibility of the machine's second breakdown. In addition, four knowledge-guided initialization methods are also designed and a reinforcement learning-based parameter adaptive strategy is used to optimize the crossover probability and mutation probability, while a knowledge-guided variable neighborhood search strategy enhances the search capability of KEARL. More importantly, three energy efficient methods are implemented to reduce the energy consumption of the production process. Finally, through extensive experiments, the KEARL is compared with several well-known algorithms. The experimental results indicate that KEARL outperforms the other algorithms.