An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.