Abstract
An assembly sequence is considered to be optimal when it minimizes the assembly cost while satisfying assembly constraints. To generate such sequences for robotic assembly, this paper proposes a method using a genetic algorithm (GA). This method denotes an assembly sequence as an individual, which is assigned a fitness related to the assembly cost. Then, a population consisting of a number of individuals evolves to the next generation through the genetic operations of crossover and mutation, based upon the fitness of the individuals. The population continues to evolve repetitively, and finally the fittest individual with its corresponding assembly sequence is found. Through case studies for industrial products such as an electrical relay and an automobile alternator, the effectiveness of the proposed method is demonstrated, and the performance is analyzed.
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