可重构制造系统重组过程中设备选择模型的研究

根据生产任务选择加工设备进行制造资源重组是实现可重构制造系统的关键问题之一，由于设备的选择涉及到多种因素，既有定量指标，又有定性指标，传统的依靠人工经验的方法显得力不从心。本文首先结合实际情况，提出了一套设备选择评价体系，通过对模糊判断矩阵采用最小对数二乘法确定各评价因素的权重系数，针对定性指标和定量指标采用不同的方法确定其性能指标值，通过模糊积分对评判指标进行综合评判，最后进行了实例研究。所提出的方法有效地简化了决策过程，为可重构制造系统设备选择提供了一套行之有效的方法。     

Fuzzy criteria for feature selection

The presence of less relevant or highly correlated features often decrease classification accuracy. Feature selection in which most informative variables are selected for model generation is an important step in data-driven modeling. In feature selection, one often tries to satisfy multiple criteria such as feature discriminating power, model performance or subset cardinality. Therefore, a multi-objective formulation of the feature selection problem is more appropriate. In this paper, we propose to use fuzzy criteria in feature selection by using a fuzzy decision making framework. This formulation allows for a more flexible definition of the goals in feature selection, and avoids the problem of weighting different goals is classical multi-objective optimization. The optimization problem is solved using an ant colony optimization algorithm proposed in our previous work. We illustrate the added value of the approach by applying our proposed fuzzy feature selection algorithm to eight benchmark problems.     

A comparison of some criteria for states selection in the latent Markov model for longitudinal data

We compare different selection criteria to choose the number of latent states of a multivariate latent Markov model for longitudinal data. This model is based on an underlying Markov chain to represent the evolution of a latent characteristic of a group of individuals over time. Then, the response variables observed at different occasions are assumed to be conditionally independent given this chain. Maximum likelihood estimation of the model is carried out through an Expectation–Maximization algorithm based on forward–backward recursions which are well known in the hidden Markov literature for time series. The selection criteria we consider are based on penalized versions of the maximum log-likelihood or on the posterior probabilities of belonging to each latent state, that is, the conditional probability of the latent state given the observed data. Among the latter criteria, we propose an appropriate entropy measure tailored for the latent Markov models. We show the results of a Monte Carlo simulation study aimed at comparing the performance of the above states selection criteria on the basis of a wide set of model specifications.     

Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management

The current paper presents a comprehensive methodology for supplier selection. In the first stage, the linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the criteria. The Axiomatic Fuzzy Set clustering (AFS) method, which handles ambiguity and fuzziness in the supplier selection problem effectively, is applied to cluster the suppliers and evaluate each potential supplier that aims at obtaining initial supplier ranking. In the second stage, the Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to determine the weight of various quantitative and qualitative criteria. To address multiple decision criteria in supplier ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to select the final suppliers. A numerical example composed of 30 suppliers and 6 criteria is studied, and the experimental results show that the proposed evaluation framework is suitable for supplier selection decisions even with the dependent criteria/attributes.     

突破性创新研发联盟最优伙伴组合方案研究

突破性创新研发联盟的研究已成为国内外学术界共同关注的重大课题,而伙伴选择是研发联盟中一个十分重要的问题,伙伴挑选的成功与否直接关系到研发联盟的合作效果和成败.在分析Bernardo方法缺陷的基础上,提出改进的Bernardo方法,进而建立具有资源约束的伙伴选择的改进Bernardo模型,并将其应用到突破性创新研发联盟最优伙伴的选择中,不仅可以求得资源约束下候选伙伴的优劣排序,还能求得参与研发联盟的最优伙伴数量和最优伙伴组合方案.     

供应链物流合作伙伴选择的多层次灰色评价方法

为了获得高效的供应链体系，物流业务外包已成为制造企业增加竞争力最重要和最有效的战略手段之一。本文研究了物流外包中的物流合作伙伴选择问题。基于制造商与物流供应商长期合作的伙伴关系，从成本、服务质量、合作稳定性以及综合实力四个方面设计了物流合作伙伴评价指标体系。针对主观的评价指标体系，建立了灰色评价与层次分析法相结合的多层次灰色评价模型，并进行了算例研究。     

A hybrid model for selection of an outsourcing provider

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness and it is important that firms scientifically select appropriate outsourcing providers. Some efforts have been made to find systematic ways to deal with outsourcing problems, but these efforts incorrectly assumed that the criteria used in the decision process are independent, which is not true in the real world. In this study, we propose a new hybrid multiple criteria decision-making (MCDM) model, which addresses the dependent relationships between the various criteria. The relations-structure among the criteria is built with the aid of the decision-making trial and evaluation laboratory (DEMATEL) method. Decision-makers tend to hold diverse opinions about their preferences due to incomplete information and knowledge, or inherent conflict between various departments. We further used the fuzzy preference programming and the analytic network process (ANP) to form a model for the selection of partners for outsourcing providers. The proposed model can help practitioners improve their decision making process, especially when criteria are numerous and inter-related. The method is demonstrated using data from a Taiwanese airline.     

A probabilistic theory for error estimation in automatic integration

Summary A probabilistic theory for derivation and analysis of error criteria for automatic quadrature is presented. In particular, conditional average error criteria are derived for quadratures which have derivative-bound error estimates. These probabilistic error criteria are compared to variations of heuristic error criteria derived by discretizing the derivative in the original error bound. It is shown that the theory provides a mathematical foundation and a quantitative model for these discrete error criteria. It is also shown that estimating the conditional average error is equivalent to testing error with the spline interpolation as a sample integrand, and that this process can be made implicit by using appropriate error criteria with local error-checks.This paper is based on the author's Ph.D. thesis in computational complexity and numerical analysis, completed at the University of California, Berkeley     

基于径向基函数网络的虚拟物流企业伙伴选择方法研究

在对虚拟物流企业系统研究的基础上 ,针对虚拟物流企业伙伴选择问题的特点 ,给出了一个面对虚拟物流企业伙伴选择问题的较为全面的选择过程框架 .并提出了一个基于径向基函数网络算法的虚拟物流企业伙伴选择模型 ,实例仿真说明了该算法和模型的有效性 .     

山区高速铁路初步设计阶段方案比选的模糊数学方法

在山区高速铁路建设中,初步设计阶段常遇到多方案比选问题.而方案选择涉及因素众多,难以定量分析.针对此问题,引入模糊综合评价模型对各方案进行比选.以渝万铁路客运专线为例,应用模糊综合评价模型对有关方案进行了比选研究,最终得到了首选方案.     

Construction and selection of quantitative planning models — a general procedure illustrated with models for stock control

The paper investigates the process of quantitative model construction when for a particular realworld decision problem different quantitative models are available. It develops first some general features for constructing a model hierarchy comprising all quantitative models which should be taken into consideration. The main objective is an investigation into the selection process giving the best quantitative model for the particular decision problem at hand. This selection, however, can be performed in different ways possibly ending up with different models. A myopic selection principle is suggested which leads to a class of strategies. Different strategies within this class are discussed showing the complexity of the selection procedure. The investigations are illustrated using inventory models particularly stressing the role of cost parameters.     

Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach

Selection of supply chain partners is an important decision involving multiple criteria and risk factors. This paper proposes a fuzzy multi-objective programming model to decide on supplier selection taking risk factors into consideration. We model a supply chain consisting of three levels and use simulated historical quantitative and qualitative data. We propose a possibility approach to solve the fuzzy multi-objective programming model. Possibility multi-objective programming models are obtained by applying possibility measures of fuzzy events into fuzzy multi-objective programming models. Results indicate when qualitative criteria are considered in supplier selection, the probability of a certain supplier being selected is affected.     

Continuous-Time Mean-Variance Portfolio Selection: A Stochastic LQ Framework

This paper is concerned with a continuous-time mean-variance portfolio selection model that is formulated as a bicriteria optimization problem. The objective is to maximize the expected terminal return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form due to the variance term involved. It is shown that this nonstandard problem can be ``embedded' into a class of auxiliary stochastic linear-quadratic (LQ) problems. The stochastic LQ control model proves to be an appropriate and effective framework to study the mean-variance problem in light of the recent development on general stochastic LQ problems with indefinite control weighting matrices. This gives rise to the efficient frontier in a closed form for the original portfolio selection problem. Accepted 24 November 1999     

Two novel FMCDM methods for alternative-fuel buses selection

In this paper, we consider the problem of alternative-fuel buses selection using two novel fuzzy multiple criteria decision-making (MCDM) methods. Several types of fuels are considered as fuel modes, i.e., electricity, fuel cell (hydrogen), and methanol. For the purpose of determining appropriate fuel buses, many factors including quantitative and qualitative ones such as efficiency, price, and capability must be taken into account. In the first proposed method, linguistic variables are used to assess the ratings and weights for factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchical MCDM method based on fuzzy-sets theory is proposed to deal with the fuel buses selection problem. To simultaneously determine the ranking order of all alternatives based on the concept of the TOPSIS by calculating the distances to the both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS), a closeness coefficient is defined. In the second presented method we extend preference selection index (PSI) method for fuzzy environment. In this method performance ratings of criteria evaluate by linguistic variable which can be expressed in trapezoidal fuzzy numbers. Finally, an example is shown to highlight the procedure of the proposed methods and compare the results of these methods with each other.     

An intuitionistic fuzzy projection-based approach for partner selection

The partner selection is an important decision problem in the formation of a new virtual enterprise. Using the projection technique, this study introduces an approach to partner selection with linguistic values and intuitionistic fuzzy information under a group decision-making environment. To avoid information loss, there is no aggregations of decision information in this model except ideal decisions as auxiliary decision tools. Comparisons of the suggested methodology with other methods are also made. A numerical example and an experimental analysis are proposed to illustrate the application of the proposed model.     

A Personnel Selection Problem in Healthcare System Using Fuzzy-TOPSIS Approach

The methods of multiple criteria decision-making (MCDM) are increasingly becoming the most desired tools for making daily decisions in various fields of human endeavors. Staff employment in any sector requires a thorough evaluation of the applicant before selection to ensure effective and efficient service delivery. Besides, healthcare is one of the most complicated organizations dealing with human lives. This paper has developed a staff selection model considering a fuzzy environment by using the technique for order preference similar to the ideal solution (TOPSIS) method. For the delivery and promotion of quality healthcare systems, medical staff selection is crucial to the system. Therefore, the study evaluates medical staff by using the expert''s linguistic judgement under the criteria of skill, experience and ability to respond to a problem. The expert''s vagueness in judgments has been represented by using fuzzy triangular numbers. The study determines the closeness coefficient, the measures of separation and the ideal solutions of the TOPSIS method. The most appropriate medical staff are ranked and selected based on the available criteria. The Fuzzy-TOPSIS method is simple and can help other organizations achieve proper ranking, evaluation and selection of qualified candidates, as it takes imprecise information into account.     

基于资源互补的项目伙伴选择方法

当不同企业承担的工程项目在同一地区同时开展时,彼此间经常会存在工程资源互补的可能,如果加以合理利用,便能够使项目的实施进一步得到优化。因此,企业如何选择有资源互补潜力的最佳合作伙伴有必要予以研究。文中从合作博弈的角度阐述了资源互补的形成机理,定性分析了伙伴选择问题的决策特征;运用双层规划构建了基于资源互补的项目伙伴选择模型,提出了基于期望收益约束选择的求解方法。最后以某临港产业园区填海项目与其他项目的资源互补为例,采用文中提出的模型及求解方法优化施工方案,得到降低建设投资、缩减施工工期的优化效果。     

Optimization of the number of components in the mixed model using multi-criteria decision-making

The distributions of empirical data are often complex. Such complexity cannot be sufficiently addressed by the individual theoretical statistical distribution function. Furthermore, the selection of the distribution function becomes more complicated when the empirical data present a multi-peak feature. In such a case, the multiple testing criteria and the mixed model must be considered during the selection of an appropriate distribution function. Aiming at this vague challenge, the present paper proposes a novel method for establishing a mixed model that can describe accurately the distribution characteristics of empirical data. Apart from combining the Akike and Bayesian information criteria to define the feasible solutions of the mixed model, this study also utilizes the root mean squared deviation, coefficient of determination, Kolmogorov–Smirnov test statistic, average deviation in cumulative distribution function, and average deviation in probability distribution function as the testing criteria. In addition, a non-linear programming is used to find the weighting factors of each criterion. The multi-criteria decision-making technology is adopted to comprehensively and objectively integrate these testing criteria into a synthetic indicator. Finally, an optimization algorithm is proposed to determine the optimal number of components in the mixed model. The illustrated results of the simulated data and measured signals confirm that this approach can estimate precisely the number of components as well as establish a highly accurate mixed model.     

LSP method and its use for evaluation of Java IDEs

In this paper we propose a quantitative model for evaluation and selection of integrated development environments (IDEs) for Java enterprise applications. Our goal is to determine the extent to which major IDEs satisfy typical software developer requirements. Our evaluation model is based on the Logic Scoring of Preference (LSP) method for system evaluation. We present an overview of the LSP method, the structure of IDE evaluation criterion, and a sample evaluation and comparison of three competitive systems: IBM WebSphere Studio Application Developer, Borland JBuilder, and SUN ONE Studio. In this paper we also introduce rectangular diagrams, an efficient new notation of LSP criteria.     

基于灰色层次分析法的航空公司机型适应性综合评价研究

运用灰色层次分析方法,对航空公司飞机机型评价过程中的几个相关因数进行了系统分析,建立了一种定量的机型综合评价模型.目的在于为航空公司进行正确的飞机选型,降低运营风险提供技术支持.通过实例分析,论证了建立的模型是可行和实用的.