Multi-objective intelligent optimization algorithm for solving dynamic production scheduling problem of distributed flow shop

The invention discloses a multi-objective intelligent optimization algorithm for solving a dynamic production scheduling problem of a printing distributed flow shop. The algorithm design is mainly embodied in that a new algorithm framework is provided, an artificial bee colony algorithm is combined with a multi-objective optimization framework, and a high-quality solution set is obtained; a dynamic optimization mode is used, so that the adverse effects of periodic maintenance of processing equipment and temporarily inserted orders on an original production scheduling plan are reduced; a core operator is improved, and the algorithm iteration efficiency is improved. Experimental results show that compared with similar solutions, the multi-target solution set with higher quality can be solved within the same time, so that the production cost is reduced, and the enterprise benefit is improved. 本发明公开了一种求解印刷分布式流水车间动态排产问题的多目标智能优化算法。本算法设计主要体现在：提出一种新的算法框架，将人工蜂群算法与多目标优化框架结合，得到高质量的解集；使用动态优化的方式，减少加工设备的周期性维护和临时插入的订单对原有排产计