摘要
为了研究不确定信息对供应商参与下的产品质量—成本控制过程的影响,基于集对分析建立了质量—成本控制的多目标贴近度优化模型。对演化细胞学习自动机算法进行适应性改进后用于求解该优化模型,并得到相对确定条件下质量—成本控制方案集合的优劣排序——基序。考虑到不确定因素的影响,利用模糊集值统计法获得差异度系数后,按照联系度对基序重新排序,进而筛选出最佳的产品质量—成本控制方案,并为每种零部件选择合理的供应商。以大型空气分离设备的质量—成本控制问题为例进行仿真计算,结果表明了所提方法的可行性与有效性。
To study influence of uncertain information on product quality cost control process with suppliers＇ involvement,Set Pair Analysis（SPA） was adopted to build the multi-objective relative degree of nearness optimization model for quality cost control.Evolutionary cellular learning automata algorithm was adaptively improved to solve the optimization model,and then quality cost control alternatives set＇s priority ordering,i.e.basic ordering,was acquired under the relatively certain condition.Considering uncertain influences,fuzzy-set-valued statistics was utilized to obtain discrepancy degree coefficient Δ,therefore the basic ordering was reordered in the light of relation coefficient,and the optimal product quality cost control concept alternative was selected.According to that optimal alternative,reasonable supplier was selected for each component.Finally,large scale deep cooling air-separating equipment＇s quality cost control process as a practical case was provided to illustrate the application and validation of the proposed method by simulation and computation.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第2期353-361,共9页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(50835008
50875237)
国家863计划资助项目(2007AA04Z190)
高等学校博士学科点专项科研基金资助项目(20070335137)~~
关键词
供应商选择
质量控制
成本控制
集对分析
不确定分析
演化细胞学习自动机
模糊集值统计
supplier selection
quality control
cost control
set pair analysis
uncertainty analysis
evolutionary cellular learning automata
fuzzy-set-valued statistics
