Research on Flexible Job Shop Scheduling Optimization Based on Improved Genetic Algorithm

Abstract

Aiming at the engineering pain points of difficult solving in practical production, slow convergence of traditional genetic algorithms, and easy getting stuck in local optima, an improved genetic algorithm with the goal of minimizing the maximum completion time has been designed. By improving coding, crossover, and introducing roulette wheel selection methods, the overall algorithm's global optimization capability is enhanced. The comparison of experimental results shows that the improved genetic algorithm is superior to the traditional genetic algorithm in optimizing the target solution.