Application of Improved Adaptive Genetic Algorithm in Flexible Workshop Scheduling

In the problem of flexible job shop scheduling, conventional genetic algorithms exhibit limited exploration capabilities and are prone to getting stuck in local optima. Consequently, this study aims to enhance the traditional genetic algorithm by adapting it to better address the scheduling problem. To do so, a corresponding mathematical model is established, taking into account the unique characteristics of flexible job shop scheduling problems. Within the genetic operations, adaptive improved crossover and mutation operators are employed, enabling non-linear adjustments based on the fitness values of each individual in the population during the iteration process. Moreover, an elitist retention strategy is implemented to minimize completion time and secure the optimal solution. Through this improved approach, the algorithm's global search ability is enhanced, convergence speed is accelerated, population diversity is increased, and optimal solutions for flexible job shop scheduling problems are obtained more quickly. Finally, the algorithm's efficiency and feasibility are demonstrated by successfully obtaining the optimal solution for the LA01 example in the workshop scheduling problem through testing..