An Efficient Tree Search Algorithm for Solving Robotic Assembly Line Balancing Problems

Abstract Traditional assembly line balancing problems (ALBP) aim to assign tasks to stations to meet specific production targets, but in practice, the problems can be extremely complex because of the additional factors, such as robot or automated equipment alternatives. The robotic assembly line problem is a broad and challenging variant of traditional lines. Designing and balancing robotic assembly lines are crucial in manufacturing to optimize productivity, efficiency, and flexibility. In this study, we propose a heuristic algorithm that provides practical and effective solutions for robotic assembly line balancing problems (RALB). We aim to assign robot and task combinations to workstations simultaneously with the objective of minimizing the system cost, including the cost of installing new robots and opening new workstations. We evaluate the algorithm’s performance on a large set of random problem instances by using statistical methods. We conclude that feasible and good solutions can be found easily, even for large-scale problems, in short processing times.