Assembly line rebalancing problem with human-robot collaboration and a hyper-matheuristic solution approach

In assembly systems where repetitive tasks are performed and standardized products are manufactured, robot utilisation has become widespread due to the influence of Industry 4.0. This trend enables robots to operate in hazardous environments and reduces human-induced errors. However, robots lack the flexibility and insight of humans. Recognising the limitations of robots, within the framework of the Industry 5.0 vision, which emphasises the importance of human-centred industries, assembly lines where humans and robots collaborate have emerged as innovative systems that allow the integration of their strengths. This interaction increases system efficiency and reduces ergonomic risks. Although only the first-time balancing is considered in studies on collaborative assembly lines, it is a costly and time-consuming process for labour-intensive organisations to completely eliminate existing systems and build a collaborative structure. In this context, it is a more operationally sustainable approach to reconfigure a line in accordance with new conditions based on the existing structure rather than building it from scratch. This study addresses the transition from assembly lines with only workers to collaborative systems. Accordingly, the Assembly Line Rebalancing Problem with Human-Robot Collaboration (ALRB-HRC) is proposed, focusing on the reconfiguration process on the basis of operators to integrate traditional and collaborative robots (cobots) in manual lines. To solve the ALRB-HRC problem, a mathematical model and a hyper-matheuristic solution method based on the artificial bee colony (ABC) algorithm are developed. The test results on benchmark problems demonstrate that the hyper-matheuristic approach outperforms the mathematical model and the basic version of the ABC algorithm.