Showing papers in "International Journal of Information Technology and Decision Making in 2014"

Multiplicative consistency of hesitant fuzzy preference relation and its application in group decision making

Abstract: As we may have a set of possible values when comparing alternatives (or criteria), the hesitant fuzzy preference relation becomes a suitable and powerful technique to deal with this case. This paper mainly focuses on the multiplicative consistency of the hesitant fuzzy preference relation. First of all, we explore some properties of the hesitant fuzzy preference relation and develop some new aggregation operators. Then we introduce the concepts of multiplicative consistency, perfect multiplicative consistency and acceptable multiplicative consistency for a hesitant fuzzy preference relation, based on which, two algorithms are given to improve the inconsistency level of a hesitant fuzzy preference relation. Furthermore, the consensus of group decision making is studied based on the hesitant fuzzy preference relations. Finally, several illustrative examples are given to demonstrate the practicality of our algorithms.

New KEMIRA Method for Determining Criteria Priority and Weights in Solving MCDM Problem

Abstract: This study presents a new KEmeny Median Indicator Ranks Accordance (KEMIRA) method for determining criteria priority and selection criteria weights in the case of two separate groups of criteria for solving multiple criteria decision making (MCDM) problem. Kemeny median method is proposed to generalize experts' opinion. Medians are calculated applying three different measure functions. Criteria weights are calculated and alternatives ranking accomplished by solving optimization problem — minimization of ranks discrepancy function calculated for two groups of criteria. A numerical example for solving specific task of elite selection from security personnel is given to illustrate the proposed method.

Information visualization to support management decisions

Abstract: Information visualization can accelerate perception, provide insight and control, and harness this flood of valuable data to gain a competitive advantage in making business decisions. Although such a statement seems to be obvious, there is a lack in the literature of practical evidence of the benefit of information visualization. The main contribution of this paper is to illustrate how, for a major European apparel retailer, the visualization of performance information plays a critical role in improving business decisions and in extracting insights from Redio Frequency Idetification (RFID)-based performance measures. In this paper, we identify — based on a literature review — three fundamental managerial functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Then, we provide — based on real industrial case evidence — how information visualization supports business decision-making. Several examples are provided to evidence the benefit of information visualization through its three identified managerial functions. We find that — depending on the way performance information is shaped, communicated, and made interactive — it not only helps decision making, but also offers a means of knowledge creation, as well as an appropriate communication channel.

Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA

Abstract: Failure mode and effect analysis (FMEA) is one of the risk analysis techniques recommended by international quality certification systems, such as ISO 9000, ISO/TS 16949, CE, and QS9000. Most current FMEA methods use the risk priority number (RPN) value to evaluate the risk of failure. The RPN value is the mathematical product of the three parameters of a failure mode that is rated between 1 and 10 in terms of its severity (S), occurrence (O), and detection (D), respectively. However, the RPN method has been found with three main drawbacks: (1) high duplicate RPN values, (2) failure to consider the ordered weights of S, O, and D, and (3) failure to consider the direct and indirect relationships between the failure modes and causes of failure. Therefore, this paper integrates the technique for order preference by similarity to ideal solution (TOPSIS) and the decision-making trial and evaluation laboratory (DEMATEL) approach to rank the risk of failure. A case of an inlet plate ring that has been drawn from a professional mechanical factory is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed method with the conventional RPN and DEMATEL methods, it was found that the proposed method can resolve the abovementioned RPN ranking issues and give a more appropriate risk assessment than other listed approaches to provide valuable information for the decision makers.

A Method Based on SMAA-Topsis for Stochastic Multi-Criteria Decision Making and a Real-World Application

Abstract: Stochastic multi-criteria acceptability analysis (SMAA-2) and the technique for order preference by similarity to ideal solution (TOPSIS) are methods for evaluating alternatives with multiple criteria. SMAA is a method that is used for solving multi-criteria decision-making problems with uncertain, inaccurate information, and does not require preference information from the decision makers. The TOPSIS method is based on the principle of determining a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution. This paper proposes a new method, SMAA-TOPSIS, by combining the SMAA and TOPSIS methods. The SMAA-TOPSIS method was executed for two problems: drug benefit-risk analysis and machine gun selection. This paper found that TOPSIS could be used with uncertain and arbitrarily distributed values for weights and criteria measurements by using a combination of SMAA and TOPSIS. Also, we obtained clearer and consistent SMAA outputs.

The Impact of Information System-Enabled Supply Chain Process Integration on Business Performance: A Resource-Based Analysis

Abstract: This paper seeks to develop and test a model to examine the relationships between, technical aspects of IS resources (IS alignment, IS resources technical quality, IS advancement), supply chain process integration, and firm performance. A questionnaire-based survey was conducted to collect data from 227 supply chain, logistics, or procurement/purchasing managers of leading manufacturing and retail organizations. Drawing on resources-based view of the firm, and through extending the concept of process integration in supply network, as well as broadening the scope of role of IS resources in relation to process integration and performance gain from the focal firm to the entire supply chain, we found that supply chain process integration is an important multidimensional intermediate organizational capability through which the value of IS resources for supply chain management can be materialized. This capability serves as a catalyst in transforming the value of technical aspects of IS resources into higher performance gain for a firm. Thus, the importance of formation of all dimensions of this capability across supply network should be realized. Moreover, the result suggests that the technical aspects of IS resources need to be jointly developed by supply partners to effectively form supply chain capabilities.

Developing e-store marketing strategies to satisfy customers' needs using a new hybrid gray relational model

Abstract: With the increasing popularity of online shopping services, e-stores are experiencing ever more fierce competition. Thus, it is imperative that managers take steps to improve their services and ensure customer loyalty, and this can only be done by understanding their customers' needs and developing appropriate marketing strategies. Therefore, the aim of this paper is to propose a new hybrid gray relational model to enhance strategies by adopting innovation and creativity to achieve the aspiration level in each dimension/criterion of satisfying/promoting human life and convenient service. This new hybrid gray relational model, which can improve the performance of each criteria to close the aspiration level of each reference point, will address interdependent problems among dimensions/criteria in the real world and provide feedback. More specifically, a DANP (DEMATEL-based ANP) and a gray relational assessment model will be combined to produce an influential network relationship map (INRM), showing the influential weights and gaps between the actual performance and the level of aspiration. As such, the contribution made by this research will be to produce the best strategies for e-store managers to improve their business model in order to meet customers' needs, encourage them to repurchase, and to enable stores to provide the most effective and efficient service for their customers.

An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem

Abstract: Companies pursuing extension of their activities and new companies in establishment phase are using various concepts and techniques to consider location decision, because location greatly affects both fixed and variable costs and on the overall profit of the company. This paper suggests a new use of quality function deployment (QFD) for facility location selection problem instead of applying it to traditional product quality promotion. Fuzzy sets concept is also incorporated to deal with imprecise nature of the linguistic judgments of decision makers. First, fuzzy QFD as a stand-alone approach is presented to address international facility location selection decision. To consider resource limitations and operational constraints, fuzzy goal programming is combined with fuzzy quality function deployment to present a developed approach to deal with global facility location-allocation decision. A demonstration of the applicability of proposed methodologies in a real-world problem is presented.

A new hybrid mcdm model combining danp with vikor for the selection of location - real estate brokerage services

Abstract: Real estate brokerage services have developed from individual stores into a chain-store system, and the location of those stores plays a key role in their operation. The purpose of this study is to define and quantify the factors that affect the selection of a site for real estate brokerage services. Mutual relationships between the factors and sub-factors for site selection and their relative weights are also discussed to provide a complete set of decision evaluation models, then how to reduce the gaps to achieve the aspiration level. This research uses a new hybrid Multiple Criteria Decision Making (MCDM) model, combining the Decision Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based Analytic Network Process (DANP), and VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) methods to solve these problems. The DEMATEL technique is used to build an influential network relations map, and DANP is expected to obtain the influential weights using the basic concept of Analytic Network Process (ANP), to solve the dependence and feedback problems in the real world. Then, the VIKOR method is used to integrate the performance gaps from criteria to dimensions and overall. As the result shows, there is an interactive and auto-feedback relationship among the four dimensions. Among the 11 evaluation criteria, the income and consumption level is the most important consideration for selection of the site. The number and density of population ranks second in this regard. This study uses VIKOR method for selection of the best site, among three potential sites. Site A is closest to the aspiration level. Site A is better in this regard than the other two sites. The study develops and provides a decision-making system for the site selection in the real estate brokerage services.

Fuzzy Multi-Objective Linear Programming Based on Compromise VIKOR Method

Abstract: Real-world decision-making problems often consist in considering multiple and antithetic objectives. Therefore, multi-objective decision making (MODM) is a practical framework in implicational areas. In this paper, a fuzzy MODM problem is considered, where all of its parameters are defined fuzzily, and a solution inspired by multi-attribute VIKOR method is proposed. The proposed method tries to find fuzzy efficient solution for a problem by minimizing its combinational distance from an ideal and anti-ideal solution. This method can reveal the efficient frontier of the problem. Applicability of the proposed method is shown in an illustrative example and its application is summarized in an investment problem. Both examples show applicability of the proposed method.

Life Insurance Companies' Performance and Intellectual Capital: A long-term perspective

Abstract: This study used dynamic data envelopment analysis (dynamic DEA) to evaluate the operating performance of life insurance companies in Taiwan and China. In addition, this study adopted panel data regression, which employs the cross-section and time-series approaches, to investigate the impact of intellectual capital (IC) on operating performance. The results indicated that the overall performance of life insurance companies in China was better than that of life insurance companies in Taiwan. Furthermore, in both countries, the performance of life insurance companies with local capital was better than that of companies with foreign capital. The results also showed that human capital (HC) and structural capital (SC) had impacts on the operating performance of life insurance companies. The potential applications and strengths of DEA in assessing the life insurance industries in Taiwan and China are highlighted.

Data envelopment analysis with missing data: a multiple linear regression analysis approach

Abstract: Data envelopment analysis (DEA) assumes that the data set is precise when performing efficiency evaluation of peer decision making units (DMUs). The current paper proposes a multiple linear regression analysis (MLRA) approach to estimate missing values if some of the entries in the data set are missing. Its algorithm to derive the estimations is also proposed. In order to verify the credibility of the proposed approach, an example of 30 US commercial banks is applied to case analysis. Using the proposed algorithm, the efficiencies of all DMUs are obtained. A Friedman test and a Kendall's Tau rank correlation analysis statistically examine the results. Moreover, the efficiency interval and efficiency distribution for a DMU are obtained considering random errors of the estimations. After that, an example of public secondary schools serves to illustrate the applications in the end.

The Inclusion-Based LINMAP Method for Multiple Criteria Decision Analysis Within an Interval-Valued Atanassov's Intuitionistic Fuzzy Environment

Abstract: The purpose of this paper is to develop an inclusion-based LINMAP (i.e., Linear Programming Technique for Multidimensional Analysis of Preference) method for multiple criteria decision analysis that is based on interval-valued Atanassov's intuitionistic fuzzy sets. Using the inclusion comparison possibility in the interval-valued Atanassov's intuitionistic fuzzy context, an inclusion-based index of interval-valued Atanassov's intuitionistic fuzzy numbers is proposed that considers positive and negative ideals. An inclusion-based consistency index and an inclusion-based inconsistency index to measure the concordance and discordance, respectively, between paired comparison judgments are suggested. An inclusion-based LINMAP model is constructed using a linear programming technique to determine the optimal criterion weights and obtain the corresponding comprehensive inclusion-based index for each alternative. Then, the priority order of the alternatives can be acquired according to the comprehensive inclusion-based indices. The feasibility of the proposed method is illustrated using a practical problem that relates to the selection of bridge construction methods. A comparative analysis of other relevant decision-making methods is conducted to validate the effectiveness of the developed methodology.

Utility indifference pricing of convertible bonds

Abstract: We propose a pricing model for convertible bonds based on the utility-indifference method and get access to the empirical results by use of Information Technology. By using the stochastic control theory, the general expression of utility indifference price on convertible bonds is obtained under the CIR interest rate model. Furthermore, using the proposed theoretical model, we present an empirical pricing study of China's market, using three convertible bonds and more than 70 months of daily market prices. The parameters value is estimated by the maximum likelihood method, and the prices of convertible bonds are simulated by the Monte Carlo approach. The empirical results indicate that the theoretical prices are higher than the actual market prices 0.24–4.58%, and the utility indifference prices are better than the Black–Scholes (BS) prices.

Identifying and Analyzing Risks and Responses for Risk Management in Information Technology Outsourcing Projects Under Fuzzy Environment

Abstract: Due to ever-increasing trend in outsourcing information technology projects in today's competitive world, the risk management in information technology outsourcing (ITO) projects is a challenging issue. Hence, this paper reviews and extracts present corresponding risks by literature review to implement risk management in ITO. After reviewing a number of frameworks in the literatures related to prioritizing of extracted risk factors, a new framework is presented to determine the priority of them. Because of network structure of the proposed framework and multi-dimensional nature of the project risk, the fuzzy analytic network process (fuzzy ANP) is applied to prioritize risk factors. Also, since identifying and prioritizing of risk factors cannot necessarily meet the organization's needs related to the project risk, the ways to respond to these factors are evaluated. For this purpose, responses to the five highest ranked risk factors are considered. Prioritization of responses to these risk factors is done by applying fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) based on four criteria: quality, cost, time, and scope. Results, achieved from experts' judgment, show that the risk factor "Supplier's lack of expertise with an IT operation" is the most significant. Also, the best response for this factor, is "Review of monetary value and volume of suppliers' contracts prior to their selection" according to experts' point of view. In addition, a sensitivity analysis is carried out for validating the results.

Credibility-based fuzzy mathematical programming model for portfolio selection under uncertainty

Abstract: In this paper, we develop a hybrid bi-objective credibility-based fuzzy mathematical programming model for portfolio selection under fuzzy environment. To deal with imprecise parameters, we use a hybrid credibility-based approach that combines the expected value and chance constrained programming techniques. The model simultaneously maximizes the portfolio return and minimizes the portfolio risk. We also consider an additional important criterion, namely, portfolio liquidity as a constraint in the model to make it better suited for practical applications. The proposed fuzzy optimization model is solved using a two-phase approach. An empirical study is included to demonstrate applicability of the proposed model and the solution approach in real-world applications of portfolio selection.

A Multicriteria Clustering Approach Based on Similarity Indices and Clustering Ensemble Techniques

Abstract: This paper deals with the problem of multicriteria clusters construction. The aim is to propose a multicriteria clustering procedure aiming at discovering data structures from a multicriteria perspective by defining a dissimilarity measure which takes into account the multicriteria nature of the problem. Comparing two objects in the multicriteria context is based on the preference information that expresses whether these objects are indifferent, incomparable or one is preferred to the other. The proposed approach uses this preference information with an agreement–disagreement similarity index to compute a dissimilarity measure. The approach generates, according to the preference relations, a set of clusterings. Each clustering expresses a way of grouping objects according to the preference relation used. A good quality final clustering is obtained by combining the clusterings generated previously using a clustering ensemble technique.

Functional integration and systems implementation of customer relationship management in hotel industry: a multilevel analysis

Abstract: Successful customer relationship management (CRM) depend on strategic skills and reflect the sharpness of long-term cooperation and organizational values. The purpose of the study will explore operational and analytical implementation of information system (IS) may lead to market strategy and CRM performances of a hotel. A CRM performance model was formulated by ISs success perspective in the study, and we collected international tourism hotel samples by mailing questionnaire survey in Taiwan. We mailed 232 questionnaires to hotels, of which 151 returned completed questionnaires and we test the model and hypotheses by structural equation modeling (SEM) and multilevel analysis for our research. As this result of the study, the role of IS will transfer from the transaction process system to a strategic supporting role, collecting information is important process to hoteliers, staffs prepare to personalize service for a guest in advance when they have more information about guest. And the capability of leveraging analytics in operations can be a critical differentiator for hotel to stay competitive. As a business discipline, this research could be directed toward helping managers and practitioners decide CRM implementation priority, and improve both business processes and competitiveness through the deployment of a CRM system.

A Novel Method for Fuzzy Multi-Criteria Decision Making

Abstract: To determine the weight vector and to aggregate the individual opinions are necessary steps in the classical methods for multi-criteria group decision-making problems in which the weight vectors of the decision makers and the criteria are incompletely known. In this paper, we propose a simple but efficient approach which can avoid these steps by establishing some optimal models. To get the optimal group decision matrix, we first propose two kinds of models among which the former focuses on minimizing the deviations between individual decision matrix and the ideal group one, while the latter aims at minimizing the deviations between the estimated group opinion and the ideal group one. To get the overall performances of alternatives, another two types of models are further established, one of which is to minimize the distance between the evaluation value under each criterion and the ideal overall value for each alternative, and the other is to minimize the distance between the estimated overall value and the ideal overall one. The proposed models can be used to deal with group decision-making under intuitionistic fuzzy, interval-valued fuzzy or other fuzzy environments, and can also provide the decision makers more choices by containing the parameter which can be assigned different values according to different actual situations. Several examples illustrate the practicability of the proposed methods.

New Direction in Degree Centrality Measure: Towards a Time-Variant Approach

Abstract: Degree centrality is considered to be one of the most basic measures of social network analysis, which has been used extensively in diverse research domains for measuring network positions of actors in respect of the connections with their immediate neighbors. In network analysis, it emphasizes the number of connections that an actor has with others. However, it does not accommodate the value of the duration of relations with other actors in a network; and, therefore, this traditional degree centrality approach regards only the presence or absence of links. Here, we introduce a time-variant approach to the degree centrality measure — time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network. We illustrate the difference between traditional and TSDC measure by applying these two approaches to explore the impact of degree attributes of a patient-physician network evolving during patient hospitalization periods on the hospital length of stay (LOS) ...

An Interactive Signed Distance Approach for Multiple Criteria Group Decision-Making Based on Simple Additive Weighting Method with Incomplete Preference Information Defined by Interval Type-2 Fuzzy Sets

Abstract: Interval type-2 fuzzy sets (T2FSs) with interval membership grades are suitable for dealing with imprecision or uncertainties in many real-world problems. In the Interval type-2 fuzzy context, the aim of this paper is to develop an interactive signed distance-based simple additive weighting (SAW) method for solving multiple criteria group decision-making problems with linguistic ratings and incomplete preference information. This paper first formulates a group decision-making problem with uncertain linguistic variables and their transformation to interval type-2 trapezoidal fuzzy numbers. Concerning the relative importance of multiple decision-makers and group consensus of fuzzy opinions, a procedure using hybrid averages is then employed to construct a collective decision matrix. By an appropriate extension of the classical SAW approach, this paper utilizes the concept of signed distances and establishes an integrated programming model to manage multi-criteria group decisions under the incomplete and inconsistent preference structure. Further, an interactive procedure is established for group decision making. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a collaborative decision-making problem of patient-centered care (PCC).

Applying linguistic information and intersection concept to improve effectiveness of multi-criteria decision analysis technology

Abstract: Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has unique strengths and weaknesses, it is difficult to determine which kind of MCDM approach is best suited to a specific problem. Therefore, a new decision-making method is proposed herein, based on linguistic information and intersection concepts; it is called the linguistic intersection method (LIM). Notably, the linguistic variables are more suited to expressing the opinion of each decision maker. There are four MCDM methods: TOPSIS, ELECTRE, PROMETHEE and VIKOR which are included in the LIM. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations of decision makers. Then, the intersection set is determined with regard to the better alternatives of all methods. Third, the final ranking order of alternatives in the intersection set can be determined by the proposed method. Lastly, an example is given to describe the procedure of the proposed method. In order to verify the effectiveness of the proposed method, a simulation test is provided to compare the LIM with the linguistic MCDM method. According to the comparison results, the proposed method is more stable in determining the ranking order of all decision alternatives.

Identification of Active Valuable Nodes in Temporal Online Social Network with Attributes

Abstract: The rapidly growing online social networks have generated great expectations connected with their potential business values. The aim of this paper is to identify the active valuable nodes that can spread business information to a large fraction of the individuals in large-scale temporal online social networks as quickly as possible. Most studies focus on static social networks, the study on the identification of active valuable nodes in temporal online social networks with quantitative attributes is still young. In this paper, we propose a method to identify active valuable nodes based on their static structural properties and temporal behavioral attributes. The method first chooses the candidates of the active valuable nodes by the static analysis of their structural properties. Then, the candidate's behavioral trend is extracted from its activity records. Through analyzing the spatio-temporal characteristics of the behavioral trend, the method distinguishes active valuable nodes from inactive ones and reveals typical evolutionary processes. We perform experiments on two practical online social networks with thousands of nodes. The experimental results demonstrate that the method can identify the active valuable nodes for information diffusion in large-scale temporal online social networks accurately and efficiently. It would be useful for business applications.

IT Project Investments: An Analysis Based on a Sort and Rank Problem

Abstract: This paper presents a multicriteria approach to the evaluation of information technology (IT) projects. In this paper, we present the steps of the model to evaluate IT project investments, including the identification of IT project investment, the construction of IT investment or IT project categories, the assessment of the importance of criteria and establishes final recommendations. The methodology proposes a framework, based on multicriteria analysis, that describes the importance of classifying and prioritizing IT projects investments before taking decisions. Decisions on IT investments should consider the different characteristics of each IT project and in each case evaluate the tangible and intangible benefits. However, measurements of intangible benefits are usually subjective and require a formal procedure to minimize the difficulty and consequence of this. Thus, the model enables us to handle this subjectiveness and it is exemplified with a numerical illustration, the data being drawn from a preview study.

Fuzzy Geometry-Based Decision Making with Unprecisiated Visual Information

Abstract: Decision making is conditioned by relevant information. This information very seldom has reliable numerical representation. Usually, decision-relevant information is perception-based. A question arises of how to proceed from perception-based information to a corresponding mathematical formalism. When perception-based information is expressed in natural language, the fuzzy set theory can be used as a corresponding mathematical formalism for decision analysis. However, perception-based decision-relevant information is not always sufficiently clear to be modeled by means of membership functions. In contrast, it remains at a level of some cloud images which are difficult to be caught by words. This imperfect information caught in perceptions cannot be precisiated by numbers or fuzzy sets and is referred to as unprecisiated information. Humans are able to make decisions based on unprecisiated visual perceptions. Modeling of this outstanding capability, even to some limited extent, becomes a difficult yet a highly promising research area. In this study, we use fuzzy geometry and the extended fuzzy logic to cope with uncertain situations coming with unprecisiated information. In this approach, the objects of computation and reasoning are geometric primitives which model human perceptions when the latter cannot be defined in terms of membership functions. For this aim, the fuzzified axioms of the incidence geometry are used. An approach to decision making with outcomes and probabilities described by geometrical primitives is developed. Examples of application of the approach to decision making on a short term investment decision and marketing decision are given. The obtained results prove the validity of the suggested approach.

Decision Support Model for Production Disturbance Estimation

Abstract: A current modeling framework for disturbance in manufacturing systems (MS) is given by concepts like discrete-event systems, stochastic fluid models and infinitesimal disturbance analysis. The goal of modeling is to achieve control and structural and functional optimization of MS. Objective functions of these optimization models are focused on quantities which reflect the level of reliability, the level of manufactured products, the quality of products or the impact on the environment of MS with disturbances. These models do not allow a dynamic evaluation of consequences of the disturbances which appears in the operation of MS machines and also do not allow an evaluation of the evolution in time of disturbance consequence indicators. Disturbances in technological lines of MS represent local bottlenecks of production with severe economic consequences in what regards production time losses. Good estimation of disturbances dynamics can be very helpful to both technological line designers, who can optimize their projects and production managers who can minimize their losses. Our model allows a dynamic evaluation of consequences of some disturbance of machine operation in MS, using indicators based on time, energy and costs. A MATLAB software package was developed for tests.

Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine

Abstract: Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.

Nonradial approach to allocating fixed-costs and common revenue using centralized dea

Abstract: In this paper, a simple approach to allocating fixed costs and common revenue among different units is presented. It is based on centralized data envelopment analysis (DEA) and uses a weighted, slacks-based, nonradial metric. The approach is units-invariant and takes into account the inefficiencies in the assessed decision making units (DMUs). The approach works when the fixed cost is a complement of another input as well as when no other inputs exist. The proposed approach is compared with existing allocation methods using several datasets from the literature.

Effects of multiple criteria on portfolio optimization

Abstract: We study the effects of considering different criteria simultaneously on portfolio optimization. Using a single-period optimization setting, we use various combinations of expected return, variance, liquidity and Conditional Value at Risk criteria. With stocks from Borsa Istanbul, we make computational studies to show the effects of these criteria on objective and decision spaces. We also consider cardinality and weight constraints and study their effects on the results. In general, we observe that considering alternative criteria results in enlarged regions in the efficient frontier that may be of interest to the decision maker. We discuss the results of our experiments and provide insights.

A Binary Option Pricing Based on Fuzziness

Abstract: In pricing for European option Black–Scholes model has been widely used in various fields in which the model can be applied under appropriate conditions. In this paper, we discuss a binary option, which is popular in OTC (Over the Counter) market for hedging and speculation. In particular, asset-or-nothing option is basic for any other options but gives essential implications for constructing more complex option products. In addition to the primary role of the asset-or-nothing option, another availability of the option is considered by introducing fuzzy concept. Therefore, the uncertainty which an investor and intermediary usually have in their minds is incorporated in the pricing model. Thus, the model is described with fuzzy boundary conditions and applied to the conventional binary option, proposing more useful and actual pricing way of the option. This methodology with the analysis is examined, comparing with Monte Carlo simulations.