A Self-Learning Memetic Algorithm for Human–Robot Collaboration Scheduling in Energy-Efficient Distributed Mixed Fuzzy Welding Shop

Due to the impact of economic globalization, distributed welding shop has become prevalent in real-world manufacturing systems. Moreover, focusing on human-centric, sustainable and resilient industry, Industry 5.0 puts more emphasis on human-robot collaboration (HRC) for its merit in promoting system flexibility and adaptability. However, owing to the instability of human performance, it becomes necessary to employ fuzzy processing time to simulate practical human production. In the context of Industry 5.0, HRC scheduling in distributed mixed fuzzy welding shop is worth exploring, but no related research on this problem is reported. Thus, to address this research gap, this paper investigates a human-robot collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC), aiming to minimize makespan and total energy consumption (TEC). To solve this issue, a self-learning memetic algorithm (SLMA) is proposed. In SLMA, a hybrid initialization is designed to yield a high-quality initial population. A genetic operator is proposed to improve the exploration capability. A self-learning variable neighborhood search (SLVNS), which hybridizes Q-learning and VNS, is developed to enhance the exploitation capability. A resource adjustment strategy is presented to further optimize TEC. Additionally, to validate the effectiveness of the proposed SLMA, extensive experimental comparisons with 5 other optimization algorithms are conducted. Experimental results illustrate that SLMA outperforms its competitors.
Note to Practitioners
—Owing to the widespread presence in manufacturing systems, distributed welding shop has attracted considerable attention in both industry and academia. In the context of Industry 5.0, the incorporation of human-robot collaboration (HRC) scheduling in distributed welding shop can promote system productivity and flexibility. Meanwhile, due to the instability of human performance, employing fuzzy processing time to simulate human production more aligns with the practical manufacturing scenario. Thus, this paper investigates a human-robot collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC). This problem model can be utilized in many welding manufacturing enterprises with HRC production mode. To solve this problem, we design a self-learning memetic algorithm (SLMA) to minimize both makespan and total energy consumption (TEC). The design of all components in SLMA is based on the characteristics of problem. The SLMA can offer the low-energy and high-efficiency schedules for practitioners. Experimental results verify the effectiveness of the proposed SLMA.