A Hybrid Genetic Branch-and-Price Method for Scheduling Optimization for Printed Circuit Board Assembly

In this paper, we proposed a hybrid genetic branch-and-price method to solve the printed circuit board assembly optimization problem with the objective of minimizing the placement time of SMT machine. The branch-and-price method is an exact algorithm that can find the optimal solution to complex optimization problems. However, it is difficult to find the optimal solution in a reasonable time when the problem size is large. Therefore, this paper proposed a genetic algorithm as the initial solution generation algorithm and a hybrid genetic ant colony optimization algorithm as the acceleration operator. In order to verify the effectiveness, the proposed algorithm is contrasted with the genetic algorithm as well as branch-and-price method respectively. The results demonstrate the effectiveness of the proposed algorithm in solving the component placement optimization problem.