A Clonal Selection Algorithm for Assembly Line Balancing Problem with Human Robot Collaboration

A typical Assembly Line Balancing (ALB) Problem aims to optimize the mass production process by assigning the tasks to stations. To deal with the high product variety and short product life cycles, robots are utilized in many production lines. Humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. This study introduces a Clonal Selection Algorithm (CSA) to solve ALB problem with human robot collaboration (HRC). Unlike the methods in the literature that define the number stations as a constant number, this algorithm is significant because it can attain the optimum number of stations to enable the minimum cycle time. The performance of the proposed CSA is tested by using well-known test problem data, originally defined for Simple ALBP. The revised data for ALBP with HRC, including cobot task time and joint tasks, are considered. CSA obtains the same best-known optimal solutions for 44.44% of the test problems and provides better solution for 18.51% of the large-sized test problems. For each data set, new solutions are introduced by defining a maximum station number and obtained results are assessed based on cycle time, completion time, gap time, number of human, and cobot. Final CSA results are illustrated with figures to visualize the idle time of the stations and the joint tasks that represent the HRC