Bio-inspired search algorithms to solve robotic assembly line balancing problems

Abstract

Robots are employed in assembly lines to increase the productivity. The objective of robotic assembly line balancing (rALB) problem is to balance the assembly line, by allocating equal amount of tasks to the workstations on the line while assigning the most efficient robot to perform the assembly task at the workstation. In this paper, bio-inspired search algorithms, viz. particle swarm optimization (PSO) algorithm and a hybrid cuckoo search and particle swarm optimization (CS-PSO), are proposed to balance the robotic assembly line with the objective of minimizing the cycle time. The performance of the proposed PSO and hybrid CS-PSO is evaluated using the 32 benchmark problems available in the literature. The simulation results show that both PSO and hybrid CS-PSO are capable of providing solutions within the upper bound obtained by hybrid GA, the only metaheuristic reported so far for rALB in the literature and comparable to the solutions obtained by IBM CPLEX Optimization solver. It is also observed that hybrid CS-PSO is performing better than PSO in terms of cycle time.