A new Fuzzy AHP method for the Evaluation of Automated Manufacturing Systems

The decision on production system acquisition for the automotive industry is very critical, given the number of different aspects to be considered. Indeed different automated solutions are feasible and evaluation techniques that take into account all the critical issues are needed to make a selection. In this paper a complete, precise and value driven Decision Support System is presented to support the selection of the best Automated Manufacturing System. The evaluation problem is solved using a Fuzzy Analytic Hierarchy Process (AHP) method able to manage uncertainty and to consider productivity and flexibility issues. Economic and financial performance and the effects on human resources due to the investment decision are also investigated.     

基于模糊层次分析的动态联盟伙伴选择策略

论述了基于模糊层次分析(AHP)的动态联盟伙伴选择策略。综合运用AHP方法和模糊决策方法构建了动态联盟伙伴选择的排序模型 ,通过求解该模型来得到参考排序值 ,力求使排序的结果能够客观、公正地反映不精确决策所蕴含的客观规律 ,以保证选择策略的准确性     

工业干燥机质量评价方法研究

本文提出了基于模糊综合评判与层次分析法相结合的干燥机质量评价方法。并将该方法用于某型号旋转闪蒸干燥机的质量评价,实证研究表明,该方法可以综合各种因素对干燥机质量的影响,帮助企业确定其产品质量,比传统的方法更加合理、有效。     

基于模糊理论的设备分类决策的研究

针对传统ABc分类方法的缺陷,利用模糊数学多属性决策理论以及层次分析法提出了基于设备重要程度的分类决策方法.该方法建立了合理的设备分类评价指标体系,利用层次分析法计算基于多部门评价的指标权重,建立基于多专家评语的单因素评价矩阵,通过模糊综合评判确定设备分类,实现了对企业设备实施分类管理的科学决策.最后给出实例说明了这一决策手段在企业的运用过程.     

基于仿真的模糊调度集成决策方法

在自动化的复杂生产环境中，决策问题是生产调度中最重要的最困难的问题之一，本文用层次分析法（AHP方法），对复杂的生产调度问题进行分解，分析，并利用仿真技术对不同的调度规则组合的调度进行仿真，最后利用模糊理论进行模糊集成决策。     

基于集对分析的绿色设计材料选择多属性决策研究

为解决绿色设计中材料选择的不确定多属性决策问题,提出一种基于集对分析的多属性决策模型。该模型应用联系数来描述材料选择方案之间的确定性与不确定性关系,分别在相对确定与不确定条件下,利用相对贴近度对方案进行综合排序,利用联系数的差异度对排序结果进行稳定性分析,并给出保持结果稳定的差异度系数取值区间的计算方法。并通过一个实例,应用该模型综合考虑材料的物理属性、经济属性和环境属性进行决策。分析结果表明,该模型是求解绿色设计材料选择不确定多属性决策问题的一种有效方法。     

机械设计专家系统中机构类型方案的评价与决策方法

评价与决策是机械设计专家系统的重要组成部分,评价准则和决策模型是评价与决策过程中的两个核心问题.本文提出以层次分析法为核心,并在对该方法进行改进的基础上来解决机械设计中机构类型方案选择的问题.最后以某自动武器方案设计中机构类型的选择为例说明了方法的应用.     

基于粗糙集扩展模型的维修级别分析决策研究

维修级别经济性分析时需要的数据多、范围广,但是往往质量差,数据不完备,其分析决策是典型的不完备信息系统决策问题。在对未知属性值分析的基础上,提出一种新的粗糙集扩展模型,给出基于该模型进行装备修理级别经济性分析的基本步骤,并通过实例介绍了决策的全过程,为解决装备维修级别经济性分析提供了新思路和新方法。     

AHP在异地协同制造协作伙伴选择中的应用

异地协同制造是一种实现快速响应市场和可持续性发展的生产模式,它强调跨企业、跨地域以有效的协作来响应用户需求,是全球化制造大背景下的企业新型生产模式,是对现有的各种生产模式的丰富和发展.文章就异地协同制造过程中有关协作伙伴的选择问题,应用层次分析法,给出了一个基于Agent的加权多目标协商决策模型及应用实例.     

金刚石合成块自动化分离方案的层次分析–模糊评价

为设计研发金刚石合成块自动化分离设备,建立自动化分离方案的评估指标体系,提出3种金刚石合成块自动化分离方案并进行层次分析。取得各项指标的权重以及各方案在单一标准下的评分后,根据层次排序结论确定分离方案。对选中的分离方案进行模糊评价。运用单因素模糊综合判断法,根据评估指标体系进行评估并最终得到了总体的评价等级。结果表明:评估指标体系的精度与实验结果相符;利用层次分析法确定的方案满足生产企业的要求。      

制造网络联盟企业合作伙伴选择方法研究

文章通过对以往伙伴选择问题的分析,得到伙伴选择是分阶段、分步骤采用多种方法进行决策和选择的结论,建立了制造网络联盟企业伙伴选择的流程和通用模型。将伙伴选择问题分大、小规模两个方面进行研究:针对大规模的选择问题,提出采用基于遗传算法和层次分析法的分阶段的合作伙伴选择方法,将选择的过程分成两个阶段进行;针对小规模的合作伙伴选择问题,引入模糊综合决策理论选择合适的合作伙伴,考虑模糊性的多个因素,组成专家组进行决策。     

Earliness-tardiness-lost sales dynamic job-shop scheduling

In the real world production environment, the decision about the acceptance or rejection of new orders is made by both the sale department and the production department, cooperatively. However, as far as we searched, no published paper has considered this fact so far. Job-shop scheduling is one of the most complex problems in scheduling. In job-shop environment, there are some production stations, and every job (order; In this paper job and order are used in the same meaning) has a specific production sequence which is not necessarily the same as the other jobs’ sequences. This paper studies the earliness-tardiness-lost sales dynamic job-shop scheduling problem. In this problem, whenever some new orders arrive, a decision is made about the acceptance or the rejection of each of these orders. In this way, all of the alternatives, including the acceptance or rejection of each new order, should be compared. If at least one new order is accepted, the new schedule which includes the accepted order(s) will be generated. By defining some variables, this comparison is done by the developed models. Because of NP-hardness of this problem, exact methods can not be used to solve it in large or medium scales. So, in this paper a hybrid metaheuristic algorithm is developed, which is composed of a genetic algorithm to determine the sequence of the operations and a simulated annealing algorithm to achieve a near-optimal schedule based on this sequence. Finally, the efficiency and effectiveness of the algorithm is evaluated using some numerical results.     

基于故障树搜索的起重机液压系统故障定位方法研究

针对起重机液压系统故障定位的问题,研究了多属性决策方法。根据功能对液压系统进行层次分解,对各子系统的不同功能工况分别建立故障树,综合分析故障树底事件故障概率、搜索成本和启发式信息量等因素,研究了故障定位方法。通过构建决策矩阵、属性值规范化、采用最小二乘法确定属性加权值、利用逼近理想解的排序方法确定了故障树底事件的最优路径搜索策略。以某型起重机吊钩不能起升故障为例进行了应用,结果表明该方法有效可行。     

Reduction of planning efforts for decision making under uncertainty in global production network design

Continuous design of production networks is an essential element to overcome historically grown, inefficient production networks, as they are common for manufacturing companies. In order to enable continuous network design, fast and low-effort methods for investment and allocation decision making are required without losing decision quality. This paper introduces a decision making approach that reduces planning efforts by systematically focusing on main influencing factors and reducing their uncertainty. The approach was applied to a real allocation decision of a machine tool manufacturer.     

The usefulness and application of fuzzy logic and fuzzy AHP in the materials finishing industry

ABSTRACT

It is suggested that fuzzy logic could occupy a more prominent role in the materials finishing industry. While a number of applications have already been made to control finishing processes and help with decision making, there is clearly scope for extending the use of fuzzy logic in the industry. After surveying some of these applications, the background to fuzzy logic is described and its set theory explained. Finally, the steps involved in selecting an environmentally acceptable metal cleaning agent from possible alternatives using a fuzzy analytic hierarchy process (AHP) are described in detail. As illustration, two different sets of selection criteria ranking are considered for choosing (i) the best solvent for cleaning equipment to be used in oxygen service and (ii) for cleaning metal parts prior to further finishing treatment.     

基于区间层次分析法的机械加工绿色工艺方案决策

针对机械加工过程中存在的不确定性,指标的权重用区间数表示更加符合实际,为此提出一种新的绿色工艺方案决策方法。建立绿色工艺方案决策指标体系,并利用区间层次分析法确定指标权重集;结合区间数理论,建立基于区间数相对优势度的绿色工艺方案决策模型;对后横梁的3套工艺方案进行决策,验证了该决策模型的可行性和实用性。     

Solving multi-criteria problems under risk: an approach explained using the example of rescheduling in dynamic environments

There is a decent number of possible heuristic methods to solve an actual problem in production planning and control. Usually, each solving method leads to a different alternative. In dynamic production environments, decision makers often have to decide between uncertainty and risk. Making multi-criteria decisions under risk is a well-known problem. In this paper, we will consider rescheduling as an example for decision-making in a dynamic production environment. It is used to present an intelligent manufacturing approach for multi-criteria decisions under risk that combines a method for decision-making under risk and a multi-attribute decision-making method. Moreover, for frequently appearing problems, such as rescheduling, a procedure to evaluate the used solving methods is presented. We use this information to achieve a sustainable improvement for the solving procedure of future manufacturing problems.     

CAPP中工序尺寸的智能决策

工序尺寸决策是CAPP系统中必不可少的内容，进行智能化决策的关键是决策方法。本文应用尺寸树与尺寸矩阵相结合的方法，从理论上、方法上、应用上对该问题进行了探讨，并给出了具体决策方法。实践证明该决策方法科学、简捷、易行。     

基于证据推理的旋转机械故障诊断模型

针对实际工况运行下的旋转机械各故障对应的量纲一的指标的范围难以严格区分的问题,提出了一种基于量纲一的指标和证据推理（Evidence Reasoning,ER）的旋转机械融合故障诊断模型。该模型利用ER算法在处理概率不确定性、模糊不确定性及非线性融合等方面的优势,通过信息变换技术将输入信号变换成信度分布结构,应用解析ER算法对输入数据进行融合,最后通过一种简单的决策规则得到诊断结果。实证分析结果表明：该方法可以有效地提高旋转机械设备故障诊断的识别率。     

Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.