The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

中文翻译：

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多目标不确定综合过程规划调度问题的改进蜜蜂交配优化算法

智能制造新技术使工艺规划和车间调度的数据更容易互联，不同制造工艺的集成优化是保障智能制造实施的重要技术。集成过程计划与调度是近年来的一个重要研究热点，它可以提高制造系统的性能。目前，对集成工艺规划与调度的研究还不足以考虑现实制造环境中广泛存在的多目标性和不确定性特征。因此，本文解决了具有不确定处理时间和截止日期的多目标集成工艺计划和调度问题。基于模糊集建立了处理时间不确定、到期日模糊的多目标不确定综合工艺规划调度问题的数学模型，设计了不确定性测量目标的计算方法。设计了一种有效的改进蜜蜂交配优化算法来求解所提出的模型。皇后集的构造是为了维护在优化过程中找到的非支配解。设计了基于欧氏距离的雄蜂与蜂王交配概率的计算方法。模糊算子被用来评估适应度，判断非支配关系，并解码调度解决方案。设计并执行了不同的实例来测试所提出方法的性能。