华侨大学学报（自然科学版）2026，Vol.47Issue(1)：68-75,8.DOI:10.11830/ISSN.1000-5013.202507005
数字孪生柔性车间中的优化调度
Optimization Scheduling in Digital Twin Flexible Job-Shop
摘要
Abstract
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.关键词
数字孪生/优化调度/遗传算法/花粉传播算法Key words
digital twin technology/optimization scheduling/genetic algorithm/flower pollination algorithm分类
信息技术与安全科学引用本文复制引用
鲁紫君,代瑶瑶,柯毅东,周林..数字孪生柔性车间中的优化调度[J].华侨大学学报（自然科学版）,2026,47(1):68-75,8.基金项目
福建省对外合作资助项目(2024I0016) （2024I0016）
福建省厦门市自然科学基金资助项目(3502Z202573043) （3502Z202573043）
