Research on flexible job-shop scheduling based on improved genetic algorithm

For the multi-objective scheduling problem in a flexible job shop, we have established a mathematical model with the objectives of maximizing the completion time and minimizing energy consumption. To address this problem, we propose an improved multi-objective genetic algorithm. Firstly, using the uniform crossover operator in the crossover process and introduce a neighborhood-based mutation operator. Secondly, improving the non-uniformity of the crossover and mutation operators to enhance the algorithm’s search capability. By dynamically adjusting the probabilities of non-uniform crossover and mutation, we increase the coverage of the search space and avoid getting trapped in local optima. Finally, testing the proposed algorithm using the Kacem benchmark test set. The experimental results demonstrate that our improved algorithm effectively solves the multi-objective scheduling problem considering both maximum completion time and energy consumption, achieving significant improvements.