A Mathematical Model and Ant Colony Algorithm for Assembly Line Balancing Problem With Human-Robot Collaboration and Alternative Subgraphs

In recent years, the use of collaborative robots in assembly lines has promised productivity improvement. It provides more alternatives for the assembly line design, which are the alternative resources of the human, robot, or human-robot collaboration (HRC), and the alternative subsets of processes, termed alternative subgraphs, taking advantage of the variety of robotic tools or end-effectors. However, more alternatives make the assembly line balancing problem more complex. This situation is encountered frequently in modern electronics and automotive assembly lines. The contribution of this study is to provide a mathematical model and solution to the assembly line balancing problem that has both HRC and alternative subgraphs, which has not been discussed as an integrated problem in previous literature. To accomplish this optimization problem, a mixed-integer linear programming (MILP) model has been developed to assign tasks to stations and determine the type of resources required while minimizing the cycle time. Practical constraints such as the available number of robots and robotic end-effector types are also considered. Owing to the complexity of the problem, the exact method for MILP is extremely time-consuming for real-world applications. Therefore, a metaheuristic algorithm based on the ant colony optimization (ACO) approach has been developed to solve the problem more efficiently. The results show that the MILP model can obtain optimal solutions for small-sized problems, whereas the ACO algorithm has proven to be a practical solution for medium- to large-sized problems, providing good solutions within an acceptable computation time. The results also show that the presence of alternative subgraphs can give opportunities for better solutions.