文献类型：期刊文献

英文题名：Multistage inventory hybrid intelligent optimization under grey fuzzy uncertainty

作者：Liu, Dongbo[1,2]; Huang, Dao[1]; Tian, Yu[1]; Chen, Yujuan[2]

机构：[1] East China University of Science and Technology, Research Institute of Automation, Meilong Rd. 130, 200237 Shanghai, China; [2] Shanghai Normal University, College of Mechanical and Electronic Engineering, Haisi Rd. 100, 201418 Shanghai, China

年份：2006

卷号：1

起止页码：514

外文期刊名：2006 International Conference on Computational Intelligence and Security, ICCIAS 2006

收录：EI(收录号：20080511069944)

语种：英文

外文关键词：Algorithms - Computer simulation - Fuzzy clustering - Neural networks - Optimal systems - Optimization

摘要：The customer demand and replenishment lead-time can be considered as grey fuzzy variables combining grey and fuzzy twofold uncertain factors. The multistage inventory model under periodical review policy was presented based on the chance measure of grey fuzzy variable. The inventory would be replenished to certain level when the inventory level drops to the re-order point. The optimal re-order point and the inventory replenishment level of every stage can be obtained by minimizing the multistage inventory cost. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the input-output data can approximate the uncertain functions. The particle swarm optimization (PSO) algorithm was improved with the differential evolution algorithm. The improved PSO algorithm was combined with neural network to optimize the inventory model. A numerical example was given to illustrate the feasibility of the model and algorithm. ? 2006 IEEE.