文献类型：期刊文献

中文题名：随机灰色提前期条件下制造/再制造混合系统库存优化

英文题名：Inventory Optimization of the Manufacturing/Remanufacturing Hybrid System with Random Grey Leadtimes

作者：刘东波[1];陈玉娟[1];黄道[2];添玉[2]

机构：[1]上海师范大学机械与电子工程学院,上海201418;[2]华东理工大学自动化研究所,上海200237

年份：2007

卷号：33

期号：4

起止页码：529

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;EI(收录号：20073810818852);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：上海市教育委员会科学研究项目资助(06DZ033)

语种：中文

中文关键词：制造/再制造;不确定提前期;库存控制;随机灰色模拟;神经网络;遗传算法

外文关键词：manufacturing/remanufacturing; uncertain leadtime; inventory control; random grey simulation; neural network; genetic algorithm

摘要：基于PUSH库存控制策略提出了在不确定生产提前期、恒定顾客需求率和产品回收率条件下的制造/再制造混合生产系统库存控制模型,可用品仓库库存由新产品制造过程和回收产品的再制造过程共同补充。不确定的生产提前期可描述为随机灰色变量,提出的随机灰色模拟技术可为不确定函数产生输入-输出数据,利用该输入-输出数据训练后的神经网络可加速不确定函数的模拟过程,由随机灰色模拟、神经网络和遗传算法集成的混合智能优化算法可求解该库存模型。数值分析结果表明:平均生产成本随给定的顾客服务水平和生产提前期的增加而增加,该不确定模型符合实际库存系统的实际情况,提出的智能优化算法可优化复杂的不确定规划问题。
This paper presents inventory control model for the manufacturing/remanufacturing hybrid system with uncertain leadtimes, the constant customer demand rate, and constant product return rate. Specifically, the PUSH inventory control strategy is studied. The serviceable inventory is simultaneously replenished from both manufacturing process and remanufacturing process. The uncertain production leadtimes are considered as random grey variables. The proposed random grey simulation methodology can generate input output data for the uncertain functions. The designed neural network trained from inputoutput data can expedite the simulation process of the uncertain functions. The hybrid intelligent optimization algorithm integrating the random grey simulation, neural network and genetic algorithm can opti mize the inventory model. The numerical analysis result indicates that the average cost increases with increasing of both the given customer service level and the average manufacturing leadtime. This result coincides with the actual situation of inventory system. The proposed solution strategy can optimize the complicated uncertain programming problems.