数字孪生柔性车间中的优化调度OA

To enhance production efficiency and reduce manufacturing costs in flexible job-shop,an improved genetic algorithm based on digital twin technology is proposed.First,a hybrid initialization strategy is em-ployed to generate a set of initial solutions.Second,a hybrid selection mechanism that combines the advanta-ges of the elite selection operator and the roulette wheel selection operator is introduced.Then,the self-polli-nation and cross-pollination operators derived from the flower pollination algorithm are embedded into the crossover and mutation stages to refine the population structure and overcome the premature convergence of conventional genetic algorithms.Finally,the algorithm is validated on the MK and Kacem benchmark instance sets as well as an actual workshop production case,while the feasibility of the resulting optimal schedules is verified within a digital twin system.The results show that the optimization performance of the improved ge-netic algorithm is enhanced by approximately 17%compared with the original genetic algorithm,demonstrating strong engineering applicability.