Effective GA Operator for Product Assembly Sequence Planning

Assembly operations of products can be determined by assembly sequence planning to meet assembly criteria. Although the genetic algorithm (GA) is one of the common algorithms used to find the optimal sequence of components in the product assembly, the optimal sequence is selected after the elimination of sequences that do not meet constraints. There is a lack of research on the effect of different types of crossover operators on GA performance. This paper introduces applications of different GA operators in the search for the optimal product assembly sequence. Four versions of GA are evaluated for their performances by employing different crossover operators in the algorithm. The roulette wheel is used as the selection mechanism. The solutions are examined by the statistical analysis in three case studies. This investigation obtains the pros and cons of each method to select the most suitable GA crossover operator for solving this specific optimization problem. © 2024, CAD Solutions, LLC. All rights reserved.