A global feedback learning-based memetic algorithm for energy-aware scheduling in collaborative heterogeneous flexible job shops

Industry 5.0 emphasizes the collaboration between human and robot resources in perception, decision-making, and execution, forming a dynamic and complementary cooperative mechanism. However, multi-flexible resource collaboration significantly enlarges the solution space, which increases the difficulty of resource allocation and optimization. At the same time, the matching of jobs with machines and the switching of operational states, such as startup and shutdown, directly affect energy consumption, making energy savings and cost reduction rigid requirements. To address these issues, a collaborative heterogeneous flexible job shop scheduling (CHFJS) model is formulated, with the primary objectives of minimizing makespan and energy consumption. Subsequently, a memetic algorithm based on global feedback learning (GFLMA) is proposed to solve the CHFJS problem. A total of 12 neighborhood structures are designed, and a Bayesian inference and weighting-based local search strategy is established. Additionally, energy-saving operators are specifically designed to address the problem characteristics. Finally, extensive experiments on instances of various scales are conducted to validate the effectiveness of GFLMA. The results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in more than 70% of the instances, confirming the superiority of GFLMA.