不确定可重入定点装配车间集成生产计划与调度

发布时间：2018-06-01 19:56

本文选题：不确定可重入 + 定点装配　；参考：《计算机集成制造系统》2017年03期

【摘要】：为满足工厂—车间一体化管理需求,研究了不确定可重入定点装配车间生产计划与调度集成优化问题。在分析车间装配特点的基础上,利用期望值描述不确定可重入情况,建立了双层生产计划与调度集成优化随机期望值模型,上层为能力约束的生产计划模型,下层为不确定可重入定点装配车间调度模型。提出了一种具有双层结构的交替迭代式改进遗传算法,上层用精英遗传算法求解生产计划,代入下层后采用基于随机模拟技术的遗传模拟退火算法求解生产调度,然后将调度结果返回上层重新求解新计划,如此不断交替迭代以实现计划与调度的同时优化。通过算例仿真验证了模型及算法的有效性。为制定不确定可重入定点装配车间生产计划与调度提供了一种合理可行的方法。
[Abstract]:In order to meet the demand of integrated factory and workshop management, the production planning and scheduling integration optimization problem of uncertain reentrant fixed point assembly shop was studied. On the basis of analyzing the characteristics of workshop assembly, using the expected value to describe the uncertain reentrant situation, a stochastic expected value model for the integration of bilevel production planning and scheduling is established, and the production planning model with capacity constraints on the upper level is established. The lower level is an uncertain reentrant fixed-point assembly shop scheduling model. In this paper, an alternating iterative genetic algorithm with double layer structure is proposed. The upper layer uses elite genetic algorithm to solve the production plan, and the lower layer is replaced by the genetic simulated annealing algorithm based on stochastic simulation technology to solve the production scheduling. Then, the scheduling results are returned to the upper layer to solve the new plan again and again, so that the scheduling and scheduling can be optimized at the same time. The effectiveness of the model and algorithm is verified by an example. It provides a reasonable and feasible method for production planning and scheduling of uncertain reentrant fixed point assembly shop.
【作者单位】：东南大学自动化学院;东南大学复杂工程系统测量与控制教育部重点实验室;南京工业大学经济与管理学院;
【基金】：国家自然科学基金重点资助项目(61673112,60934008) 中央高校基本科研业务费资助项目(2242014K10031) 江苏高校优势学科建设工程资助项目江苏省教育厅高校哲学社会科学研究资助项目(2016SJB630025)~~
【分类号】：TB497

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