数字孪生柔性车间中的优化调度

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中图分类号：TP301.6；F406.6 文献标志码：A 文章编号： 1000-5013（2026）01-0068-08

Abstract：To enhance production eficiency 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 employed to generate a set of initial solutions.Second，a hybrid selection mechanism that combines the advantagesof the elite selection operator and the roulete wheel selection operator is introduced.Then，the self-polination 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 genetic algorithm is enhanced by approximately 17% compared with the original genetic algorithm，demonstrating strong engineering applicability.

Keywords：digital twin technology；optimization scheduling；genetic algorithm；flower pollination algorithm

数字孪生系统构建“物理-虚拟"闭环体系，其虚实交互、动态模拟的特性为解决复杂的生产调度问题提供了新的途径。（剩余8822字）