An integrated logarithmic fuzzy preference programming based methodology for optimum maintenance strategies selection

Selecting optimum maintenance strategies plays a key role in saving cost, and improving the system reliability and availability. Analytic hierarchical process (AHP) is widely used for maintenance strategies selection in the Multiple Criteria Decision-Making (MCDM) field. But the traditional or hybrid AHP methods either produce multiple, even conflict priority results, or have complicated algorithm structures which are unstable to obtain the optimum solution. Therefore, this paper proposes an integrated Logarithmic Fuzzy Preference Programming (LFPP) based methodology in AHP to solve the optimum maintenance strategies selection problem. The multiplicative constraints and deviation variables are applied instead of additive ones to utilize both qualitative and quantitative data, and process the upper and lower triangular fuzzy judgments to obtain the same priorities. The proposed methodology can produce the unique normalized optimal priority vector for fuzzy pairwise comparison matrices, and it is capable of processing all comparison matrices to obtain the global priorities simultaneously and directly in the form of super-matrix according to the different requirements and judgments of decision-makers. Finally, an example is provided to demonstrate the feasibility and validity of the proposed methodology.     

Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty

This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.     

求解资源受限项目调度问题的约束规划/数学规划混合算法

利用约束规划（constraintprogramming,CP）与数学规划（mathematicalprogramming,MP）结合的方法求解调度问题已经获得了一些较好的研究成果,正成为调度问题研究领域的一个新的热点研究方向．本文针对求解资源受限项目调度问题（RCPSP）的整数规划模型,设计了基于CP技术的问题和模型预处理方法,证明了整数规划模型的有效不等式定理,提出了通过将项目子网络图转化为加权最大团问题求解后获得有效不等式的方法．引用标准问题库PSPLIB中的一组典型问题进行求解实验,结果表明本文提出的有效不等式可以明显改进模型的求解质量和时间性能．论文最后对实验结果进行了深入讨论,讨论了未来的研究方向．     

A mathematical programming method for formulating a fuzzy regression model based on distance criterion.

Fuzzy regression models are useful to investigate the relationship between explanatory and response variables with fuzzy observations. Different from previous studies, this correspondence proposes a mathematical programming method to construct a fuzzy regression model based on a distance criterion. The objective of the mathematical programming is to minimize the sum of distances between the estimated and observed responses on the X axis, such that the fuzzy regression model constructed has the minimal total estimation error in distance. Only several alpha-cuts of fuzzy observations are needed as inputs to the mathematical programming model; therefore, the applications are not restricted to triangular fuzzy numbers. Three examples, adopted in the previous studies, and a larger example, modified from the crisp case, are used to illustrate the performance of the proposed approach. The results indicate that the proposed model has better performance than those in the previous studies based on either distance criterion or Kim and Bishu's criterion. In addition, the efficiency and effectiveness for solving the larger example by the proposed model are also satisfactory.     

A novel fuzzy linear regression model based on a non-equality possibility index and optimum uncertainty

Various kinds of fuzzy regression models are introduced in the literature and many different methods are proposed to estimate fuzzy parameters of the models. In this study, a new approach is introduced to find the parameters of a linear fuzzy regression, with fuzzy outputs, the input data of which is measured by crisp numbers. Based on a non-equality possibility index, a new objective function is designed and solved, by which a minimum degree of acceptable uncertainty (the h-level or h-cut) is found. Four numerical examples are presented to compare the proposed approach with some other methods. Results show superiority of the new approach based on the criterion used by Kim and Bishu in the cases studied here. A realistic application of the proposed method is also presented, by which the total energy consumption of the Residential-Commercial sector in Iran is modeled using three variables of the GDP, number of the Households and an Energy Price index as inputs (exogenous variables) to the model.     

采购决策支持系统中模型自动选择技术的研究

针对采购者往往不是计算机专业人员,不知道如何根据药品销售历史数据的特点在采购决策支持系统中选取适当的模型来进行预测的问题,本文采用一种基于遗传算法求解树结点的线性权值矢量,并根据树节点的错误率与分割后的错误率减少量构造二元决策树的方法,来实现采购决策支持系统中模型自动选择,最后仿真实验证明文中方法构造的二元决策树能正确的完成模型的自动选择。     

Fault isolation in manufacturing systems based on learning algorithm and fuzzy rule selection

Neural Computing and Applications - This paper addresses the fault detection and isolation problem in manufacturing systems. Some of these systems can be affected by several faults, a first way of...     

Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

一种基于三段规则的模糊温度控制器

采用了一种三段规则的模糊控制策略,对真空烧结炉的温度进行控制。经仿真实验证明：各项指标均优于一般的模糊控制方式,取得了令人满意的控制效果。     

模糊层次分析法在方案优选方面的应用

给出了一种实用的层次分析方法(AHP),即模糊层次分析法(FAHP),讨论了它的原理和步骤。特别是模糊层次分析法,它是基于模糊集合理论的。它采用模糊一致关系去实现模糊推导,使用模糊一致矩阵去实现动态自适应。分析了该方法的适用范围,并提供了具有一般特点的方案优选的实例。提出了模糊层次分析方法的不足之处。     

基于多Agent的网络化制造资源优化配置DSS

文章针对网络化制造中的制造资源优化配置问题,提出了基于多Agent的网络化制造资源优化配置决策支持系统(NMROCDSS)。首先对系统功能需求进行了分析,讨论了NMROCDSS系统同其它系统的集成,在此基础上研究了系统体系结构,最后分析了系统的决策过程。     

A fuzzy decision support system for digital camera selection based on user preferences

Mobile phones have been the most rapidly spreading development in the field of communication and information technologies over the past decades. Nowadays, digital cameras have taken their place. The wide product range in the market, each with numerous heterogeneous technical attributes, complicates the selection of the most convenient camera for end-users. The aim of this work is to provide end-users with a decision support framework for selecting the best digital camera according to their preferences. End-users and photography experts use subjective assessments when determining their requirements and making their evaluations. The proposed decision support tool is built on the basis of fuzzy set theory. The imprecision of the subjective assessments are transformed to fuzzy triangular numbers. The fuzzy analytic hierarchy process (FAHP) and fuzzy compromise programming methodologies are applied in order to determine the relative weights of sub-criteria and criteria and to rank the digital camera alternatives, respectively.     

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

The aim of this paper is to develop a general post-processing methodology to reduce the complexity of data-driven linguistic fuzzy models, in order to reach simpler fuzzy models preserving enough accuracy and better fuzzy linguistic performance with respect to their initial values. This post-processing approach is based on rule selection via the formulation of a bi-objective problem with one objective focusing on accuracy and the other on interpretability. The latter is defined via the aggregation of several interpretability measures, based on the concepts of similarity and complexity of fuzzy systems and rules. In this way, a measure of the fuzzy model interpretability is given. Two neuro-fuzzy systems for providing initial fuzzy models, Fuzzy Adaptive System ART based and Neuro-Fuzzy Function Approximation and several case studies, data sets from KEEL Project Repository, are used to check this approach. Both fuzzy and neuro-fuzzy systems generate Mamdani-type fuzzy rule-based systems, each with its own particularities and complexities from the point of view of the fuzzy sets and the rule generation. Based on these systems and data sets, several fuzzy models are generated to check the performance of the proposal under different restrictions of complexity and fuzziness.     

A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange

The aim of this study is to construct appropriate portfolios by taking investor’s preferences and risk profile into account in a realistic, flexible and practical manner. In this concern, a fuzzy rule based expert system is developed to support portfolio managers in their middle term investment decisions. The proposed expert system is validated by using the data of 61 stocks that publicly traded in Istanbul Stock Exchange National-100 Index from the years 2002 through 2010. The performance of the proposed system is analyzed in comparison with the benchmark index, Istanbul Stock Exchange National-30 Index, in terms of different risk profiles and investment period lengths. The results reveal that the performance of the proposed expert system is superior relative to the benchmark index in most cases. Additionally, in parallel to our expectations, the performance of the expert system is relatively higher in case of risk-averse investor profile and middle term investment period than the performance observed in the other cases.     

Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing

The problem of selecting a target formation(s) in a reservoir among a vast number of zones/sub-layers within huge number of hydrocarbon producing wells for hydraulic fracturing (HF) by using interval type-2 fuzzy logic system (IT2-FLS) to maximize their net present value is studied in this paper. Classical fuzzy system which is called type-1 fuzzy logic system is not capable of accurately capturing the linguistic and numerical uncertainties in the terms used and the inconsistency of the expert’s decision-making. IT2-FLS is very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set; hence it is very effective for dealing with uncertainties. In highlighting this need, the question has been answered why IT2-FLS should be used in this study. The procedure of applying this study in the area of HF candidate-well selection is illustrated through a case study in an oil reservoir.     

A decision support system for coating selection based on fuzzy logic and multi-criteria decision making

Wear and corrosion are the most important factors that the surface of the engineering parts must confront. The need for protection and improvement of the mechanical characteristics of the surface of engineering parts can be to some extent satisfied by coatings. Coatings are considered as an excellent solution when resistance to corrosion, oxidation or low friction is demanded, but due the complexity of selecting the appropriate one, engineers often avoid them. The need for simultaneous consideration of qualitative and quantitative properties, render the use of classic material selection theories inadequate. An expert system for coating selection is presented in this paper, which can handle both qualitative and quantitative variables. The mathematical model used combines the multi-criteria decision making theories (MCDM) together with the fuzzy sets theory. The “Max-Min set” method is applied to calculate the ordering value of the alternatives while the TOPSIS method is used to rank them. A numerical example is provided to illustrate the method. Finally, the process presented can be easily computerized, to create the relative software.     

A systematic fuzzy rule based approach for fault classification in transmission lines

The paper presents a new approach for fault classification in transmission line using a systematic fuzzy rule based approach. Fault classification is one of the important requirements in distance relaying for identifying the accurate phases involved in the fault process. The proposed technique starts with preprocessing the fault current signal using advanced time–frequency transform such as S-transform to compute various statistical features. After the required features are extracted, the Decision Tree (DT), a knowledge representation method, is used for initial classification. From the DT classification boundaries, the fuzzy membership functions (MFs) and corresponding fuzzy rule-base is developed for final classification. Thus a systematic fuzzy rule base is developed for fault classification, reducing the redundancies and complexities involved compared to Heuristic fuzzy rule-based approach. Also a qualitative comparison is made between S-transform and Wavelet transform, where S-transform based DT-fuzzy provides highly improved results compared to the later during simulation as well as experimental tests.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

一个基于市场分析与预测的决策支持系统MAF-DSS

文章介绍一个用于市场分析与预策的决策支持系统MAF-DSS。它从管理信息系统中提取数据,运用数学方法与人工智能方法,对市场情况进行多种分析与预测,并将结果综合成文字报告,利用语音同时配以表格、统计图形、可视化模型、三维复杂模型实时动态显示等多种形式输出,为企业的管理与决策提供支持。文章在简述系统功能后,着重介绍这一系统的解决方案和系统所采用的分析预测方法与框架。     

Combination of online clustering and Q-value based GA for reinforcement fuzzy system design

This paper proposes a combination of online clustering and Q-value based genetic algorithm (GA) learning scheme for fuzzy system design (CQGAF) with reinforcements. The CQGAF fulfills GA-based fuzzy system design under reinforcement learning environment where only weak reinforcement signals such as "success" and "failure" are available. In CQGAF, there are no fuzzy rules initially. They are generated automatically. The precondition part of a fuzzy system is online constructed by an aligned clustering-based approach. By this clustering, a flexible partition is achieved. Then, the consequent part is designed by Q-value based genetic reinforcement learning. Each individual in the GA population encodes the consequent part parameters of a fuzzy system and is associated with a Q-value. The Q-value estimates the discounted cumulative reinforcement information performed by the individual and is used as a fitness value for GA evolution. At each time step, an individual is selected according to the Q-values, and then a corresponding fuzzy system is built and applied to the environment with a critic received. With this critic, Q-learning with eligibility trace is executed. After each trial, GA is performed to search for better consequent parameters based on the learned Q-values. Thus, in CQGAF, evolution is performed immediately after the end of one trial in contrast to general GA where many trials are performed before evolution. The feasibility of CQGAF is demonstrated through simulations in cart-pole balancing, magnetic levitation, and chaotic system control problems with only binary reinforcement signals.