Application of Particle Swarm Optimization to Solve Robotic Assembly Line Balancing Problems

Assembly line balancing (ALB) problems mainly deal with proper allocation of tasks to the workstations in a balanced manner without violating the precedence relationship and optimizing a given objective function. This problem mainly occurs in a continuous production line and is classified as one of the hard optimization problems. Since the installation of assembly line is a long-term decision and highly cost intensive, there is a proper need of designing the assembly line and balancing the workload at the workstations. Over the years, human workforce has been replaced by robots for performing assembly tasks in the industries. Different types of robots with different capacity and specialization are available there is high requirement of selecting the best-fit robot to perform the tasks in the assembly line. Hence, this leads to the development of robotic assembly line balancing (RALB) problems. In this chapter, detailed implementation procedure for using metaheuristics to solve RALB problems with an objective of minimizing the cycle time is presented. Two configurations of robotic assembly line (straight and U-shaped) are discussed in detail. Particle swarm optimization (PSO) is used to solve the problem, experimental results obtained by using PSO algorithm are presented, and detailed discussion of the findings is reported.