![]() Human–robot collaboration (HRC) is an emerging technology in the field of novel production systems. To remain competitive, manufacturing enterprises have to introduce new production concepts to increase their performance. Humans comprise characteristics like flexibility, adaptability, decision making skills, and creativity while strength, endurance, speed, and accuracy are attributes of robots (Helms et al. Consequently, economy of scope is achieved by manual assembly utilizing human advantages in manufacturing corporations and small- and medium-sized enterprises (SMEs) (Antonelli et al. In the current state, however, manufacturers cannot efficiently automate many tasks as the established robot technology does not provide the required degree of flexibility. As a result of the evolving role of automation technology, enterprises predominantly focused on achieving economy of scale by standardization of processes and inclusion of industrial robots (Hu et al. Additionally, the use of highly automated assembly lines assures standardized product quality and process safety (Boysen et al. This growth is mainly driven by the automotive and electronics industry (International Federation of Robotics 2018), where industrial robots are utilized in assembly lines to ensure the companies’ ability of high-volume production at low costs. In 2017, for instance, worldwide robot sales reached 374,000 units, an increase of 217% compared to 2010. Several types of automated equipment, such as industrial robots, are frequently included in production systems (Graves and Redfield 1988). The role of automation in modern manufacturing companies has increased significantly over the past decades. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. ![]() ![]() Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. ![]()
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