An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

A fuzzy outranking relation in multicriteria decision making

It frequently happens that a decision maker must establish a ranking within a finite set of alternatives with respect to multiple criteria. The subjective evaluation of each alternative according to each criterion is expressed in the form of a distributive evaluation. To capture the preferences of one alternative over another, a concept of fuzzy outranking relation can be used. This fuzzy outranking relation is characterized by a degree of credibility which is computed from two indices: a confidence index and a doubt index. Each of these indices is calculated from the distributive evaluations over the various criteria. In this paper, such a fuzzy outranking relation (fuzzy binary relation) is constructed and an application is presented.     

A multicriteria decision support tool for modelling bank credit ratings

We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

     

Spatial fuzzy consumer's decision making: A multicriteria analysis

This paper is devoted to a multicriteria analysis of the consumer's behavior when the decision maker is acting in a fuzzy space and manifesting an imprecise attitude. At first, the process of decision making is described with the help of three relationships between the set of goods which are supplied in several locations, the set of their characteristics and the set of the consumer's a priori possible behaviors. All these relations are fuzzy. The model applies the theory of fuzzy relations equations. Then, the stages of the decision process are analyzed. Often fuzzy behavior relations are like ‘black boxes’. The mathematical solution of the model indicates in which conditions their valuations are possible. The main interest of this method is not to use additive operations on subjective items and to use operators which are coherent with the fuzzy nature of the variables.     

A method for solving fuzzy multicriteria decision problems with dependent criteria

We propose a new multi-criteria decision making (MCDM) method based on fuzzy pair-wise comparisons and a feedback between the criteria. The evaluation of the weights of criteria, the variants as well as the feedback between the criteria is based on the data given in pair-wise comparison matrices. Extended arithmetic operations with fuzzy numbers are used as well as ordering fuzzy relations to compare fuzzy outcomes. An illustrating numerical example is presented to clarify the methodology. A special SW-Microsoft Excel add-in named FVK was developed for applying the proposed method. Comparing to other software products, FVK is free, able to work with fuzzy data and utilizes capabilities of widespread spreadsheet Microsoft Excel.     

Modeling subjective evaluation for fuzzy group multicriteria decision making

This paper presents a new fuzzy multicriteria decision making (MCDM) approach for evaluating decision alternatives involving subjective judgements made by a group of decision makers. A pairwise comparison process is used to help individual decision makers make comparative judgements, and a linguistic rating method is used for making absolute judgements. A hierarchical weighting method is developed to assess the weights of a large number of evaluation criteria by pairwise comparisons. To reflect the inherent imprecision of subjective judgements, individual assessments are aggregated as a group assessment using triangular fuzzy numbers. To obtain a cardinal preference value for each decision alternative, a new fuzzy MCDM algorithm is developed by extending the concept of the degree of optimality to incorporate criteria weights in the distance measurement. An empirical study of aircraft selection is presented to illustrate the effectiveness of the approach.     

Preference disaggregation and statistical learning for multicriteria decision support: A review

Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields.     

A fuzzy opportunity and threat aggregation approach in multicriteria decision analysis

Economic expansion in developed countries coupled with dramatically growing economies in countries such as China and India have precipitated a steady increase in demand for oil and natural gas. The Caspian Sea region holds large quantities of both oil and natural gas. Because the Caspian Sea is landlocked and the region’s nations are distant from the largest energy markets, transportation must at least begin by pipeline. While some lines currently exist, pipelines with the capacity of transporting larger amounts of energy resources must be constructed to meet the global demand. This study is conducted for a multinational oil and natural gas producer to develop a multicriteria decision analysis (MCDA) framework for evaluating five possible pipeline routes in the Caspian Sea region. The proposed MCDA model considers a large number of conflicting criteria in the evaluation process and captures decision makers’ (DMs’) beliefs through a series of intuitive and analytical methods such as the analytic network process and fuzzy scoring. A defuzzification method is used to obtain crisp values from the subjective judgments and estimates provided by multiple DMs. These crisp values are aggregated and synthesized with the concept of entropy and the theory of the displaced ideal. The alternative routes are plotted on a diagram in a polar coordinate system and a classification scheme is used along with the Euclidean distance to measure which alternative is closer to the ideal route.     

Intelligent multicriteria decision support: Overview and perspectives

This paper presents a survey of the history and the recent status of the Multicriteria Decision Support Systems (MCDSSs). The last generation of MCDSSs is based on the synergistic operation of Artificial Intelligence and Multicriteria Analysis (MA) approaches. The contribution of these two different approaches into an integrated MCDSS is analysed through this paper, which concludes providing the recent software developments in this field.     

Aggregation of fuzzy preference relations to multicriteria decision making

Weighted aggregation of fuzzy preference relations on the set of alternatives by several criteria in decision-making problems is considered. Pairwise comparisons with respect to importance of the criteria are given in fuzzy preference relation as well. The aggregation procedure uses the composition between each two relations of the alternatives. The membership function of the newly constructed fuzzy preference relation includes t-norms and t-conorms to take into account the relation between the criteria importance. Properties of the composition and new relation, giving a possibility to make a consistent choice or to rank the alternatives, are proved. An illustrative numerical study and comparative examples are presented.     

Fuzzy multicriteria decision support for budget allocation in the transport sector

Summary This paper compares two ways of providing decision support for the allocation of a fixed financial budget among a set of competing highway investment proposals. The first, which is described only in outline, uses a broadly conventional, hierarchically structured linear additive multicriteria model. The technical focus of the paper, however, is on the second, and approach based in fuzzy multicriteria modelling. The thinking which led us to explore this approach is set out, together with the formal structure of the model. The results of a small case study are given and an assessment is made of how decision makers' understanding of the investment options available can be enhanced by using the two models in tandem.     

A novel two-factor forecasting model for fuzzy time series

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improving forecasting accuracy, however, the issue of partitioning intervals has rarely been investigated. Recently, we proposed a novel deterministic forecasting model to eliminate the major overhead of determining the k-order issue in high-order models. This paper presents a continued work with focusing on handling the interval partitioning issue by applying the fuzzy c-means technology, which can take the distribution of data points into account and produce unequal-sized intervals. In addition, the forecasting model is extended to allow process twofactor problems. The accuracy superiority of the proposed model is demonstrated by conducting two empirical experiments and comparison to other existing models. The reliability of the forecasting model is further justified by using a Monte Carlo simulation and box plots. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Natural discrete-event process forecasting: A decision support system

A decision support system for the analysis and forecasting of natural discrete-event processes is considered. The corresponding method is based on the sample path analysis using event-to-event operations. An application of the method in the computer-aided decision support system for the long-range weather forecasting is mentioned.     

A multicriteria decision model for assigning priority classes to activities in project management

Problems related to the management of construction projects are addressed in many studies. Falling behind schedule and being over budget are examples of bad results, due to the uncertainties and the dynamic environment of the construction process. This study proposes a decision model for helping project managers to focus on the main tasks of a project network during the life cycle of a project based on a MCDA (multiple criteria decision analysis) method. The model assigns priorities classes to activities in project management, taking into account several points of view. The model is based on the ELECTRE TRI-C method, which permits activities to be assigned to categories. As the environment is very dynamic, the model was built taking into consideration changes that may occur while a project is being carried out, and therefore the model must be reassessed during the project life cycle. Furthermore, the model supports a decision making environment where responsibilities are distributed amongst project team members and it brings the benefit of developing disciplines that lead to the team??s greater effectiveness. An application of the model, based on a realistic situation, is presented in the context of a construction project in order to demonstrate the use of the model. The results show that by using the model, managers can improve their performance with regard to controlling project activities.     

A fuzzy multiobjective programming algorithm in decision support systems

Multicriteria analysis is one of the analytical functions in the problem processing system of decision support systems (DSS). In this paper, an interactive and iterative fuzzy programming method for solving a quasi-optimization problem in complex decisions under constraints involving a multiple objective function is proposed. Comparing with an adapted gradient search method, a surrogate worth tradeoff method, and a Zionts—Wallenius method, an approximate preference structure is emphasized in the proposed method.     

IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection

A fundamental principle of modern portfolio theory is that comparisons between portfolios are generally made using two criteria, corresponding to the first two moments of return distributions, namely the expected return and portfolio variance. According to this model and according to most of the portfolio models derived from the stochastic dominance approach, the group of portfolios open to comparisons is divided into two parts: on the one hand there are the efficient portfolios (those that are not dominated by any other portfolio in the group), and on the other, those that are dominated. In other words, these models do not solve for one optimal portfolio, but rather solve for an efficient set of portfolios, among which the investor must choose, given his preference system. One criticism over these models, which has often been addressed both by practitioners and academics, is that they fail to embody the objectives of the decision maker (DM), through the various stages of the decision process. Our purpose in this article is to present an integrated and innovative methodological approach for the construction and selection of equity portfolios, which will take into account the inherent multidimensional nature of the problem, while allowing the DM to incorporate his preferences in the decision process. The proposed approach, which grounds its basis on the field of multiple criteria decision making (MCDM) and more specifically on multiobjective mathematical programming (MMP), is implemented in the IPSSIS (Integrated Portfolio Synthesis and Selection Information System) decision support system (DSS). The validity of the proposed approach is tested through an illustrative application in the Athens Stock Exchange (ASE).     

A new decision support model in multi-criteria decision making with intuitionistic fuzzy sets based on risk preferences and criteria reduction

In this paper, we propose a new model for decision support to address the ‘large decision table’ (eg, many criteria) challenge in intuitionistic fuzzy sets (IFSs) multi-criteria decision-making (MCDM) problems. This new model involves risk preferences of decision makers (DMs) based on the prospect theory and criteria reduction. First, we build three relationship models based on different types of DMs’ risk preferences. By building different discernibility matrices according to relationship models, we find useful criteria for IFS MCDM problems. Second, we propose a technique to obtain weights through discernibility matrix. Third, we also propose a new method to rank and select the most desirable choice(s) according to weighted combinatorial advantage values of alternatives. Finally, we use a realistic voting example to demonstrate the practicality and effectiveness of the proposed method and construct a new decision support model for IFS MCDM problems.     

A comprehensive decision support model for the evaluation of eco-designs

Due to regulatory pressures from government and non-government bodies and public awareness of the need to protect the environment, incorporating sustainability concerns in product design has become a key strategic consideration in new product development. However, selecting an appropriate sustainable design solution is a challenging task. In addition to the fact that such a decision involves conflicting objectives, there is also the issue that environmental impact considerations can occur at all stages of a product’s life cycle. Modelling and assessing new product development and operations management from a life cycle assessment (LCA) perspective is becoming increasingly popular and highly important. However, on its own it is somewhat limited. This paper presents a dynamic approach that integrates LCA, fuzzy logic and analytical network process (ANP) to support the selection of environmental sustainable product designs. A numerical example is provided as an operational guideline on how to apply it to LCA of eco-designs. The results show that the proposed fuzzy ANP approach is a viable methodology and can be used as an effective tool for the evaluation of environmental sustainable product designs.