Decision making support system for house purchase by using fuzzy logic

In this paper, we suggest a decision making support system for house purchasers, using fuzzy inference and hierarchic structure of evaluation. Main part of this system consist of macro and micro evaluation. Essential factors are taken into account in macro evaluation, and unessential detailed factors are considered later in micro evaluation. By adopting this structure, many decision makers could get their most suitable result.     

Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach

Bankruptcy filings are as high today as ever, calling into question the efficacy of existing bankruptcy prediction models. This paper tries to provide an alternative for bankruptcy prediction by using neuro fuzzy, a hybrid approach combining the functionality of fuzzy logic and the learning ability of neural networks. The empirical results show that neuro fuzzy demonstrates a better accuracy rate, lower misclassification cost and higher detecting power than does logit regression, meaning neuro fuzzy could be a great help in providing warnings of impending bankruptcy. Also, its comprehensive explanation about mapping functions among variables presumably provides a foundation for further development of theory and testing of the membership function shape, the transfer function, the methods to aggregate, the methods to defuzzify, and so on.     

Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP

The lack of consistency in decision making can lead to inconsistent conclusions. In fuzzy analytic hierarchy process (fuzzy AHP) method, it is difficult to ensure a consistent pairwise comparison. Furthermore, establishing a pairwise comparison matrix requires judgments for a level with n criteria (alternatives). The number of comparisons increases as the number of criteria increases. Therefore, the decision makers judgments will most likely be inconsistent. To alleviate inconsistencies, this study applies fuzzy linguistic preference relations (Fuzzy LinPreRa) to construct a pairwise comparison matrix with additive reciprocal property and consistency. In this study, the fuzzy AHP method is reviewed, and then the Fuzzy LinPreRa method is proposed. Finally, the presented method is applied to the example addressed by Kahraman et al. [C. Kahraman, D. Ruan, I. Do?an, Fuzzy group decision making for facility location selection, Information Sciences 157 (2003) 135-153]. This study reveals that the proposed method yields consistent decision rankings from only n − 1 pairwise comparisons, which is the same result as in Kahraman et al. research. The presented fuzzy linguistic preference relations method is an easy and practical way to provide a mechanism for improving consistency in fuzzy AHP method.     

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.     

A fuzzy ANP approach to shipyard location selection

It is the first duty of investors to transform financial resources into investments in the right places at the right times and earn benefits. However, where to invest and how to invest is always a risky and complicated problem. The decision which will be made under a variety of factors might result in a terrible mistake if it is not felicitous. It is necessary to utilize scientific methods when deciding to make these types of complicated investments which can cause large financial losses. Analytical network process (ANP) can show us the best alternative based on different criteria from a variety of alternatives. However, the network structure should model the actual in a very good way. Analytical network process will give us the best result in span after the correctly built network structure’s comparisons are made correctly. It is important to design the structure flexibly in this work in order for it to lead to improvements. Shipyard location is selected in this work which provides reference to investors based on numerical and solid essences. At the end, the study has received a successful application with the help of expert people and the correct fuzzy analytical network process’s success over the criteria which have a close relationship with each other.     

Development of fuzzy process accuracy index for decision making problems

Several statistical decision making tools and methods are available to organize evidence, evaluate risks, and aid in decision making. Process capability indices are the summary statistics to point out the process performance. In this paper, these indices are analyzed to obtain a new decision making tool. Process accuracy index (C_a) measures the degree of process centering and gives alerts when the process mean departures from the target value. It focuses on the location of process mean and the distance between mean and target value. We modify the traditional process accuracy index to obtain a new tool under fuzziness. With the proposed tool, specification limits and process mean can be defined as triangular or trapezoidal fuzzy numbers. The proposed tool is illustrated to solve a supplier selection problem.     

Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey

Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been widely used to evaluate alternative strategies in order to determine the best one for given business setting. This study aims at providing a quantitative basis to analytically determine the ranking of the factors in SWOT analysis via a conventional multi-criteria decision making method, Analytic Network Process (ANP). The ANP method is preferred in this study because of its capability to model potential dependencies among the SWOT factors. The study presents uniqueness in the way it incorporates inherent vagueness and uncertainty of the human decision making process by means of the fuzzy logic. The proposed SWOT fuzzy ANP methodology was implemented and tested for the Turkish airline industry. The results showed that the SWOT fuzzy ANP is a viable and highly capable methodology that provides invaluable insights for strategic management decisions in the Turkish airline industry, and can also be used as an effective tool for other complex decision making processes.     

Decision making in fuzzy discrete event systems

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert’s knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.     

Mediative fuzzy logic: a new approach for contradictory knowledge management

In this paper we are proposing a novel fuzzy method that can handle imperfect knowledge in a broader way than Intuitionistic (in the sense of Atanassov) fuzzy logic does (IFL). This fuzzy method can manage non- contradictory, doubtful, and contradictory information provided by experts, providing a mediated solution, so we called it Mediative Fuzzy Logic (MFL). We are comparing results of MFL, with IFL and traditional Fuzzy logic (FL).     

An ontology-based fuzzy decision support system for multiple sclerosis

The use of Magnetic Resonance (MR) as a supporting tool in the diagnosis and monitoring of multiple sclerosis (MS) and in the assessment of treatment effects requires the accurate determination of cerebral white matter lesion (WML) volumes. In order to automatically support neuroradiologists in the classification of WMLs, an ontology-based fuzzy decision support system (DSS) has been devised and implemented. The DSS encodes high-level, specialized medical knowledge in terms of ontologies and fuzzy rules and applies this knowledge in conjunction with a fuzzy inference engine to classify WMLs and to obtain a measure of their volumes. The performance of the DSS has been quantitatively evaluated on 120 patients affected by MS. Specifically, binary classification results have been first obtained by applying thresholds on fuzzy outputs and then evaluated, by means of ROC curves, in terms of trade-off between sensitivity and specificity. Similarity measures of WMLs have been also computed for a further quantitative analysis. Moreover, a statistical analysis has been carried out for appraising the DSS influence on the diagnostic tasks of physicians. The evaluation has shown that the DSS offers an innovative and valuable way to perform automated WML classification in real clinical settings.     

The fuzzy approach to statistical analysis

For the last decades, research studies have been developed in which a coalition of Fuzzy Sets Theory and Statistics has been established with different purposes. These namely are: (i) to introduce new data analysis problems in which the objective involves either fuzzy relationships or fuzzy terms; (ii) to establish well-formalized models for elements combining randomness and fuzziness; (iii) to develop uni- and multivariate statistical methodologies to handle fuzzy-valued data; and (iv) to incorporate fuzzy sets to help in solving traditional statistical problems with non-fuzzy data. In spite of a growing literature concerning the development and application of fuzzy techniques in statistical analysis, the need is felt for a more systematic insight into the potentialities of cross fertilization between Statistics and Fuzzy Logic. In line with the synergistic spirit of Soft Computing, some instances of the existing research activities on the topic are recalled. Particular attention is paid to summarize the papers gathered in this Special Issue, ranging from the position paper on the theoretical management of uncertainty by the “father” of Fuzzy Logic to a wide diversity of topics concerning foundational/methodological/applied aspects of the integration of Fuzzy Sets and Statistics.     

A fuzzy quality control-decision support system for improving operational reliability of liquid transfer operations in laboratory automation

Quality control in liquid transfer operations can eliminate false assay results due to transfer failures, monitor the stability of laboratory equipment, provide baselines for continuous improvement, and enable automatic recovery actions for a number of fault conditions. This paper describes the work done to add real-time quality control capability in liquid transfer operations on a closed-loop controlled MEMS based liquid transfer device.Real-time data acquisition enables the interpretation of system variables. Using fuzzy logic as a decision support tool, an overall quality metric and failure mode is deduced from these variables for each channel per operation. Detection of ordinary fault conditions like clogged tips or unexpected empty source wells can activate automatic recovery actions without human intervention, leading to 24/7 utilization of an automated laboratory system. Exported quality data can be used for bio-informatics data analysis as well as documentation for regulatory agencies.     

Incorporating fuzzy operators in the decision network to improve classification reliability

One type of hierarchical fuzzy-operator-based network implementation is investigated. In this approach, we generalized the Dombi operator as an effective component for decision analysis and making. This methodology provides several advantages due to the fact that the input to each node is the evidence supplied by the degree of satisfaction of sub-criteria and the output is the aggregated evidence. Thus, the decision making process is to aggregate and propagate the evidence information through such a hierarchical network. This trainable network is able to perceive and interpret complex decisions by using those transparent fuzzy models. This study examines the behavior of the fuzzy additive operator in more detail and the results show that the proposed framework exhibits reliable decision in the pattern classification domain.     

Achieving quality assurance functionality in the food industry using a hybrid case-based reasoning and fuzzy logic approach

Quality control of food inventories in the warehouse is complex as well as challenging due to the fact that food can easily deteriorate. Currently, this difficult storage problem is managed mostly by using a human dependent quality assurance and decision making process. This has however, occasionally led to unimaginative, arduous and inconsistent decisions due to the injection of subjective human intervention into the process. Therefore, it could be said that current practice is not powerful enough to support high-quality inventory management. In this paper, the development of an integrative prototype decision support system, namely, Intelligent Food Quality Assurance System (IFQAS) is described which will assist the process by automating the human based decision making process in the quality control of food storage. The system, which is composed of a Case-based Reasoning (CBR) engine and a Fuzzy rule-based Reasoning (FBR) engine, starts with the receipt of incoming food inventory. With the CBR engine, certain quality assurance operations can be suggested based on the attributes of the food received. Further of this, the FBR engine can make suggestions on the optimal storage conditions of inventory by systematically evaluating the food conditions when the food is receiving. With the assistance of the system, a holistic monitoring in quality control of the receiving operations and the storage conditions of the food in the warehouse can be performed. It provides consistent and systematic Quality Assurance Guidelines for quality control which leads to improvement in the level of customer satisfaction and minimization of the defective rate.     

Analysis of assembly operations difficulty: a fuzzy expert system approach

Typical mechanical products can be assembled in various sequences of assembly operations. These sequences have high impact on the assembly time, machine utilization and even on the product quality. In order to select the best sequence of assembly operations, proper evaluation of the various sequences of assembly operations is required. This, in turn, requires the consistent evaluation of each assembly operation in the sequence. The assembly operations can be evaluated for various criteria, of which the operation difficulty is the most meaningful. This paper describes a methodology to analyse the assembly operations and calculates an operation's degree of difficulty using an expert system. This analysis consists of two steps: the first one identifies the main parameters that affect the assembly difficulty and assigns fuzzy triangular values to these parameters. The second step assigns weights to the parameters in order to maximize the agreement with a domain expert. The expert system analyses the difficulty of the assembly operation performed in two orientations: horizontal and vertical. The expert system then assigns a triangular fuzzy number as the aggregate measure of the operation's difficulty.     

A review of soft consensus models in a fuzzy environment

In the consensus reaching processes developed in group decision making problems we need to measure the closeness among experts’ opinions in order to obtain a consensus degree. As it is known, to achieve a full and unanimous consensus is often not reachable in practice. An alternative approach is to use softer consensus measures, which reflect better all possible partial agreements, guiding the consensus process until high agreement is achieved among individuals. Consensus models based on soft consensus measures have been widely used because these measures represent better the human perception of the essence of consensus. This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges.     

A fuzzy logic approach for dealing with qualitative quality characteristics of a process

Dealing with qualitative information is quite common in real life problems. So far research focused on developing process adjustment models only for quantitative data. This paper presents a process adjustment approach of a deteriorating process in which quality characteristics are expressed in qualitative form. This approach jointly determines the initial setting of process mean and production run. A fuzzy logic is adopted to implement the process adjustment approach. The features of this approach are lack of mathematical complexity and ability to deal with qualitative data. Detailed implementation of the fuzzy process adjustment model is also given in this paper.     

Assessment of the risk of occupational accidents using a “fuzzy” approach

This paper presents an assessment method of the risks of occupational accidents based on fuzzy logic. The purpose of this work was to create a methodological instrument that can semiquantitatively assess the risk of occupational accidents for different industrial and site activities and to identify the most efficient intervention measures that can be taken to reduce risks. This approach, which is new in the assessment of the risk of occupational accidents, allows qualitative, imprecise and in some cases contradictory data to be elaborated, as the data that describe an occupational accident usually are. The set-up analysis model therefore allows (1) an assessment to be made of the level of risk of a work phase and/or a work sector, (2) a verification and quantification to be made of the reduction of the risk after having adopted preventive and/or protective measures and (3) a priority of interventions to be established on the basis of the assessed risk levels. It can be seen how the method, here applied to a company producing industrial vehicle tyres, can be considered applicable for any type of company, with the only prerequisite of having a record of sufficient and homogeneous number of accidents so as to be able to correctly instruct the software and tune the reference parameters.     

Using fuzzy decision making system to improve quality-based investment

In this paper, fuzzy set theory is used to select the quality-based investment in small firm. Here a new algorithm, which will consider both exogenous and endogenous variables as factors, is proposed to formulate the problem. The structure of the algorithm is based on fuzzy decision-making system (FDMS), which uses fuzzy control rules. Hence, one exogenous factor and five endogenous factors mentioned above are determined as input variables and fuzzified using membership function concept. Then, the weights of these factors are fuzzified to ensure the consistency of the decision maker when assigning the importance of one factor over another. Applying IF-THEN decision rules, quality-based investments are scored. Also the comparison with Analytical Hierarchy Process (AHP) and Fuzzy Linguistic Approach (FLA) in respect to these scores is presented.     

Integrating analytical network process and fuzzy logic to prioritize the strategies - A case study for tile manufacturing firm

SWOT analysis is the most important tool for decision makers in strategic management process, because it enables the managers to discover and collect the facts that resulted from internal and external analysis. This analysis can be a good base for strategy formulation, but it is not non-defect. It includes no means of analytically determining the importance of factors or assessing the fit between SWOTS factors and decision alternatives and is mainly based on the qualitative analysis. Many researchers develop different techniques to quantify the SWOT analysis, but these methods are not comprehensive. In this study, we develop a fuzzy method model for quantitative weighing of SWOT strategies. The decision maker’s imprecise information is regarded by fuzzy numbers in this model. To consider dependency among factors and sub-factors, we use ANP technique in our research. We demonstrate our model with a case study example and compare results of our research with prior research.