Group Decision-making Technique Research Articles

Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment

Many maintenance approaches have been developed and applied successfully in a variety of sectors such as aviation and nuclear industries over the years. Some of those have also been employed in the maritime industry such as condition-based maintenance; however, choosing the best maintenance approach has always been a big challenge due to the involvement of many attributes and alternatives which can also be associated with multiple experts and vague information. In order to accommodate these aspects, and as part of an overall novel Reliability and Criticality Based Maintenance strategy, an existing fuzzy multiple attributive group decision-making technique is employed in this study, which is further enhanced with the use of Analytical Hierarchy Process to obtain a better weighting of the maintenance attributes used. The fuzzy multiple attributive group decision-making technique has three distinctive stages, namely rating, aggregation and selection in which multiple experts’ subjective judgments are processed and aggregated to be able to arrive at a ranking for a finite number of maintenance options. To demonstrate the applicability in a real-life industrial context, the technique is exemplified by selecting the best maintenance approach for shipboard equipment such as the diesel generator system of a vessel. The results denote that preventive maintenance is the best approach closely followed by predictive maintenance, thus steering away from the ship corrective maintenance framework and increasing overall ship system reliability and availability.

A group decision classifier with particle swarm optimization and decision tree for analyzing achievements in mathematics and science

Group decision making is a multi-criteria decision-making method applied in many fields. However, the use of group decision-making techniques in multi-class classification problems and rule generation is not explored widely. This investigation developed a group decision classifier with particle swarm optimization (PSO) and decision tree (GDCPSODT) for analyzing students' mathematic and scientific achievements, which is a multi-class classification problem involving rule generation. The PSO technique is employed to determine weights of condition attributes; the decision tree is used to generate rules. To demonstrate the performance of the developed GDCPSODT model, other classifiers such as the Bayesian classifier, the k-nearest neighbor (KNN) classifier, the back propagation neural networks classifier with particle swarm optimization (BPNNPSO) and the radial basis function neural networks classifier with PSO (RBFNNPSO) are used to cope with the same data. Experimental results indicated the testing accuracy of GDCPSODT is higher than the other four classifiers. Furthermore, rules and some improvement directions of academic achievements are provided by the GDCPSODT model. Therefore, the GDCPSODT model is a feasible and promising alternative for analyzing student-related mathematic and scientific achievement data.

Prioritization of Design Requirements for Quality Engineering Education

The educational institutions must strive to impart quality education and have to create greater satisfaction in their customer group. Quality Function Deployment (QFD) which is a customer driven tool in implementing Total Quality Management (TQM) helps to accomplish this task. One of the phases in QFD methodology is known as House of Quality (HoQ), which is concerned with translating the voice of customer into design requirements by stakeholders. Design requirements will determine how the customer needs are to be fulfilled. This paper presents an integrated methodology (HoQ-ANP) to translate Voices of Customer (VoC) or customer needs (CNs) into design requirements (DRs) and to determine the importance weights of DRs by considering the complex dependency relationships between and within Customer needs and DRs for total quality in engineering education. In order to deal with the vagueness, uncertainty and diversity in dependency relationships fuzzy set theory and group decision-making technique are used to determine the priority structure of CNs, inner dependence among Customer Needs (CNs), Inner dependence among DRs and inter-relationship between CNs & DRs. Prioritization of design requirements for quality engineering education is determined through a case study by employing HoQ-ANP methodology.

Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches

As a hot topic, financial distress prediction (FDP), or called as corporate failure prediction, bankruptcy prediction, acts as an important role in decision-making of various areas, including: accounting, finance, business, and engineering. Since academic research on FDP has gone on for nearly eighty years, there are abundant literatures on this topic, which may appear chaotic to the researchers of the field and make them feel confused. This paper contributes to the current review researches by making a full summary, analysis and evaluation on the current literatures of FDP. The current literatures of FDP are reviewed from the following four unique aspects: definition of financial distress in the new century, FDP modeling, sampling approaches for FDP, and featuring approaches for FDP. By considering the new state-of-the-art techniques in this area, FDP modeling are classified and reviewed by the following groups: namely, modeling with pure single classifier, modeling with hybrid single classifier, modeling by ensemble techniques, dynamic FDP modeling, and modeling with group decision-making techniques. Sampling methods for FDP are classified and reviewed by the following paired groups, namely: training sampling and testing sampling, single industry sampling and cross-industry sampling, balanced sampling and imbalanced sampling. Featuring methods for FDP are categorized and reviewed by qualitative selection and combination of qualitative and quantitative selection. We comment on the current researches from the view of each category and propose further research topics. The review paper is valuable to guide research and application of the area.

DFAHP MULTICRITERIA RISK ASSESSMENT MODEL FOR REDEVELOPING DERELICT PUBLIC BUILDINGS

Reusing abandoned public buildings is a positive strategy in sustainable urban development. An appropriate assessment method is needed to reduce the risks of redeveloping derelict public properties. The Delphi method is an optimal group decision-making technique; whereas the analytical hierarchy process (AHP) method is useful for solving multicriteria decision-making problems. In addition, fuzzy logic manages artificial uncertainty and ambiguity, where an explicit number or ratio can express the level of preference. This study uses the Delphi method, fuzzy logic, and AHP (DFAHP) as a risk assessment model to redevelop derelict public buildings. The DFAHP provides an objective reference for investment decisions and is beneficial in reducing the risk of the public sector investing in the reuse of abandoned public buildings, in aiding in reuse cases that revitalize urban economic development, and in appreciating the value of sustainable city development.

Determining and Prioritizing the Organizational Determinants of Emergency Medical Services (EMS) in Iran

BackgroundImproving the organization of pre-hospital emergency to provide emergency medical services (EMS), as a part of health system, plays an important role in timely and properly response to incidents, as well as, reducing mortalities and disabilities.ObjectiveThis study was conducted to determine the organizational determinants of emergency medical services in Iran and analyze their relationship and prioritize them.Materials and MethodsThe present study is kind of descriptive and cross-sectional study that has been conducted on the first half of 2010 using DEMATEL method (a group decision-making technique). Required data were collected using a questionnaire from a sample of 30 Iranian experts in pre-hospital emergency, who were selected using available sampling method.ResultsThe determinants of establishing an independent EMS organization as a policy maker and observer organization, providing services through public organizations such as Emergency 115, private organizations partnership in pre-hospital emergency system, and integrating pre-hospital and hospital emergency under single supervision and management were determined as organizational determinants. Also, establishing an independent EMS organization and integrating pre-hospital and hospital emergency under single supervision and management were determined as the most affecting and affected organizational determinants, respectively, with the coordinates (1.01 and 1.01) and (0.85 and - 0.85) in the pre-hospital emergency organizational determinants graph.ConclusionsEmergency medical services should be considered as a system with its independent components. Establishing an independent EMS organization, integrating pre-hospital and hospital emergency under single supervision and management, as well as, extending the possibility of providing EMS through private sector are essential in order to make fundamental reforms in providing emergency medical services in Iran.

How to handle uncertainties in AHP: The Cloud Delphi hierarchical analysis

In practice, many practical problems occur in uncertain environments, especially in situations that involve human subjective evaluation such as that in the analytic hierarchy process (AHP). This paper presents a practical multi-criteria group decision-making method for decision making under uncertainty. To handle the randomness and fuzziness of individual judgments, the normal Cloud model, group decision-making technique, and the Delphi feedback method are adopted. In the proposed Cloud Delphi hierarchical analysis (CDHA), experts are asked to express their judgments using interval numbers. Individual fuzziness and randomness are then mined from the interval-value comparison matrices. Subsequently, the interval-value pairwise comparison matrices are converted into the corresponding Cloud matrices, and the one-iteration Delphi process is executed to diminish individual judgment mistakes. The individual Cloud weight vectors are calculated using the geometric mean technique and are finally weighted to form the group Cloud weight vector. A simple case study that involved reproducing the relative area sizes of six provinces in China shows that the CDHA method can effectively reduce mistakes and improve decision makers’ judgments in situations that require subjective expertise and judgmental inputs. In addition, a practical decision-making problem in which houses are ranked by home buyers shows that the proposed method is effective when applied to complex, large, multidisciplinary problems with considerable uncertainties.

A Multi-Criteria Evaluation Model for Developmental Effectiveness in Cultural and Creative Industries

Artificial intelligence is a scientific tool that is both supportive and has many applications and has been widely applied in different domains and industries. In the globalize competitive environment, raising firm competitiveness and reducing investment risk are serious issues that must be confronted by decision makers. Artificial intelligence helps enhance industrial development by providing the benefits of lending support to management, aiding in decision making, assisting in the design process, and helping in practical applications. In recent years, Taiwan has been actively promoting the implementation of the cultural and creative industries in developments from various sectors, and has established relevant subsidy policies. The goal is to improve Taiwan's influence in the knowledge economy. Selecting a set of proper tools to evaluate effectiveness in industrial development and policy promotion will help enhance overall execution efficiency and improve final results. This research uses the fuzzy logic theory in artificial intelligence to process the imprecise and vague linguistic meanings in decision making. By further combining the group decision-making technique from Delphi, we developed a multi-criteria evaluation model for developmental effectiveness in the cultural and creative industries. This model evaluates the developmental effectiveness of the inputs from each sector into the cultural and creative industries through first performing a quantified value transformation process by the fuzzy logic inference system. Decision making units may base the evaluation upon the quantified values. Lastly, this model is presented via a case study to illustrate the use of this model and to demonstrate the capability of the model.

Dynamic RAT selection for multiple calls in heterogeneous wireless networks using group decision-making technique

Existing radio access technology (RAT)-selection algorithms for heterogeneous wireless networks (HWNs) do not consider the problem of RAT selection for a group of calls from a multimode terminal (MT). Multimode terminals (MTs) for next generation wireless networks have the capability to support two or more classes of calls simultaneously. When a new call is initiated on an MT already having an ongoing call in an HWN, the current RAT may no longer be suitable for the two calls (incoming call and the existing call). Thus, a new RAT may be more suitable for the two calls. The problem of RAT selection for two or more calls from an MT in an HWN is a group decision problem. This paper addresses the problem of RAT selection for a group of calls from an MT in an HWN by using the modified TOPSIS group decision-making technique. The paper proposes a dynamic RAT-selection algorithm that selects the most suitable RAT for a single call or group of calls from an MT in an HWN. The algorithm considers users’ preferences for individual RATs, which vary with each class of calls, in making RAT selection decisions in an HWN. A user’s preference for each of the available RATs is specified by weights assigned by the user to RAT selection criteria for different classes of calls. Based on the assigned weights, the proposed algorithm aggregates individual calls’ weights specified by the user to make a RAT-selection decision for a group of calls. In order to reduce the frequency of vertical handover, the proposed algorithm uses RAT preference margin in making RAT selection decisions. RAT preference margin is a measure of the degree to which the newly preferred RAT is better than the current RAT. Performance of the proposed algorithm is evaluated through numerical simulations. Results are given to show the effectiveness of the proposed RAT-selection algorithm.

A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems

The purpose of this paper is to demonstrate how the fuzzy and stochastic approaches as well as multi-attribute group decision-making techniques can be effectively utilized for selection problems in different kinds of uncertainty situations. A group of the decision makers describes a value for an alternative with respect to an attribute by the use of linguistic variables. The rating of each alternative is aggregated, which can be expressed as triangular fuzzy numbers. Then Monte Carlo simulation is applied to generate the performance distribution of alternatives with respect to different attributes in an uncertain environment. Finally, a stochastic compromise ranking technique, known as the VIKOR technique, is developed to evaluate probability distributions for each alternative on each attribute and to prioritize them. The proposed fuzzy stochastic approach is suitable to compromise a solution among the conflicting tangible and intangible attributes under the group decision-making process. In addition, an application of the proposed approach is provided to solve the important risk selection problem in a highway project. The results illustrate its real-life applicability and suitability in discrete group decision-making under uncertainty.

An integrated approach for rating engineering characteristics’ final importance in product-service system development

Product-service system (PSS) approach has emerged as a competitive strategy to impel manufacturers to offer a set of product and services as a whole. A three-domain PSS conceptual design framework based on quality function deployment (QFD) is proposed in this research. QFD is a widely used design tool considering customer requirements (CRs). Since both product and services influence satisfaction of customer, they should be designed simultaneously. Identification of the critical parameters in these domains plays an important role. Engineering characteristics (ECs) in the functional domain include product-related ECs (P-ECs) and service-related ECs (S-ECs). ECs are identified by translating customer requirements (CRs) in the customer domain. Rating ECs’ importance has a great impact on achieving an optimal PSS planning. The rating problem should consider not only the requirements of customer, but also the requirements of manufacturer. From the requirements of customer, the analytic network process (ANP) approach is integrated in QFD to determine the initial importance weights of ECs considering the complex dependency relationships between and within CRs, P-ECs and S-ECs. In order to deal with the vagueness, uncertainty and diversity in decision-making, the fuzzy set theory and group decision-making technique are used in the super-matrix approach of ANP. From the requirements of manufacturer, the data envelopment analysis (DEA) approach is applied to adjust the initial weights of ECs taking into account business competition and implementation difficulty. A case study is carried out to demonstrate the effectiveness of the developed integrated approach for prioritizing ECs in PSS conceptual design.

Multicriteria Group Decision-Making Technique for a Low-Class Road Maintenance Program

Low-class roads substantially differ from main roads in terms of extensiveness, databank availability, and profitability, which stresses the need for an extraordinarily sophisticated decision-makin...

Priority determination in strategic energy policies in Turkey using analytic network process (ANP) with group decision making

In developing countries, energy consumption is continuously increasing in parallel to the developing technology. Turkey is one of these countries with its increasing population and energy demand, which increases every year with a very high rate, that is, 8% annually. Moreover, along with the increasing energy demand, the strategic energy policies have to be analyzed scientifically including the geographical importance for the realization phase. It is necessary to prioritize the determined policies and plan them according to the economic situation of the country. Hence, alternative energy policies have been prioritized objectively with scientific methods including the related institutions' evaluation. In this study, a model based on analytic network process, a group decision-making technique, is proposed for evaluating strategic energy policies. The model is used to assign priorities to strategic energy policies of Turkey. With this model, various people are included in the decision-making process and the effectiveness of the process is increased. Copyright © 2008 John Wiley & Sons, Ltd.

GROUP-BASED FAILURE EFFECTS ANALYSIS

This paper presents the multi-based experts Failure Effects Analysis (FEA). The experts' opinions differ substantially because the experts do not often agree on the level of the failure factors (failure probability, non-detection probability, severity of effect, and expected cost) and the functions/subsystems attributes (e.g., importance). Therefore, conflict always occurs in Group-based Failure Effects Analysis (GFEA). The approach uses fuzzy Risk Priority Category (RPC) and group decision-making techniques to study both the failure effects on the functions/subsystems and the failure risk category with uncertain information. In addition, the approach uses the compensated operators to allow the tradeoffs either among failure factors or among functions/subsystems attributes. A solved example is presented to demonstrate the Group-based Failure Effects Analysis (GFEA) application.

An integrated multi-objective optimisation and fuzzy multi-attributive group decision-making technique for subdivision arrangement of Ro–Ro vessels

This paper focuses on developing a new multiple criteria decision-making (MCDM) methodology for subdivision arrangement of Ro–Ro vessels, which integrates the multi-objective optimisation with a fuzzy multi-attributive group decision-making (FMAGDM) technique. The study concentrates on the task of finding and then evaluating (or ranking) the finite number of pareto-optimal design alternatives (PODAs). A genetic algorithm based multi-objective optimisation technique, namely MOGA, is employed for optimisation purpose in terms of chosen design parameters such as damage stability, survivability, and cargo capacity. MOGA is the methodology where the solution space is searched for a set of PODAs, from which experts can express their opinions and choose the best PODA. The subjectiveness and imprecision of the ranking process is modelled as linear trapezoidal fuzzy numbers by means of linguistic terms. An attribute based aggregation technique for homogeneous and heterogeneous groups of experts is employed and used for dealing with the fuzzy opinion aggregation. Finally, a real case study on the subdivision arrangement of a Ro–Ro vessel is conducted to illustrate the effectiveness of the approach.

Integrating AHP and data mining for product recommendation based on customer lifetime value

Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers’ needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.

Development of a methodology for selecting criteria and indicators of sustainable forest management: a case study on participatory assessment.

This paper describes an application of multiple criteria analysis (MCA) in assessing criteria and indicators adapted for a particular forest management unit. The methods include: ranking, rating, and pairwise comparisons. These methods were used in a participatory decision-making environment where a team representing various stakeholders and professionals used their expert opinions and judgements in assessing different criteria and indicators (C&I) on the one hand, and how suitable and applicable they are to a forest management unit on the other. A forest concession located in Kalimantan, Indonesia, was used as the site for the case study. Results from the study show that the multicriteria methods are effective tools that can be used as structured decision aids to evaluate, prioritize, and select sets of C&I for a particular forest management unit. Ranking and rating approaches can be used as a screening tool to develop an initial list of C&I. Pairwise comparison, on the other hand, can be used as a finer filter to further reduce the list. In addition to using these three MCA methods, the study also examines two commonly used group decision-making techniques, the Delphi method and the nominal group technique. Feedback received from the participants indicates that the methods are transparent, easy to implement, and provide a convenient environment for participatory decision-making.

Cognitive biases, risk perception, and venture formation: How individuals decide to start companies

Despite the high risk involved, thousands of individuals decide to start ventures. Past research, however, has found that entrepreneurs do not have a high-risk propensity, that is, a great willingness to knowingly take risks. This study, therefore, explores how individuals cope with the risks inherent in their decisions, and suggests that entrepreneurs may not perceive the riskiness of starting ventures. The study's findings suggest that risk perceptions may differ because certain types of cognitive biases lead individuals to perceive less risk. Cognitive biases are common types of mental shortcuts used to make judgments. This study examines three cognitive biases that previous research has suggested may lower risk perception. The first, overconfidence, refers to the failure to know the limits of one's knowledge. The second bias tested, the illusion of control, occurs when individuals overemphasize the extent to which their skill can increase performance in situations where chance plays a large part and skill is not necessarily the deciding factor. Because the individuals believe that they can control largely uncontrollable events, they also think they can accurately predict the outcome of the events. Finally, the third bias, the belief in the law of small numbers occurs when an individual uses a limited number of informational inputs (a small sample of information) to draw firm conclusions. This study's sample consisted of 191 students pursuing a Masters of Business Administration. The students' responses to a survey based on a case study regarding a decision to start a venture were examined. The survey included questions about the students' willingness to start the venture, their perception of the venture's riskiness, and the extent to which they exhibited cognitive biases in their decision processes. The study's findings tentatively suggest that individuals start ventures because they do not perceive the risks involved, and not because they knowingly accept high levels of risks. The belief in the law of small numbers lowered an individual's perceptions of a venture's riskiness, suggesting that some individuals draw firm conclusions from small samples. An illusion of control also decreased risk perception, suggesting that individuals starting ventures might not acknowledge that certain tasks, important to the venture's success, are beyond their control. Some argue that biases might be associated with venture failure. If this is the case, the very processes that increase the likelihood of starting a venture may actually decrease performance. Entrepreneurs may choose to minimize their biases by soliciting and paying heed to the advice of outsiders, or by using group decision-making techniques, such as devil's advocacy or dialectical inquiry. Others, however, suggest that early in the decision process, biases may be beneficial because they lower risk perception, which allows entrepreneurs to generate the commitment needed for success. Even if this is true, entrepreneurs should still institute processes to increase learning so the venture can adjust to unfolding realities and avoid any damage caused by initial misperceptions. Similarly, entrepreneurs need adequate safety nets in case their biases lead them to encounter unforeseen difficulties. The potential positive and negative effects of biases and perceiving low levels of risk suggest the importance of exploring this area further.

The Measurement of Distortion Tendencies Induced by the Win-Lose Nature of In-group Loyalty

A great deal has been written about the pressing need to put people back into the decisionmaking process and to rely less on machines, models, and quantitative methods for management decisions. Considerable research has been devoted to the task of identifying the relative advantages and disadvantages of the more popular group decision-making techniques such as interacting groups, nominal groups, and Delphi groups. The purpose of this inquiry is the measurement of the distortion effects of group loyalty on the decision-making behavior of nominal groups.

Structured Decision Making in Public Organizations

The nominal group technique (NGT) and other structured group decision-making techniques are increasingly used for public planning, budget setting, and policy making. An abundance of case studies report successful applications of the techniques, but NGT's assumptions about group processes and about politics should be more thoroughly questioned. This research investigates the claims made for the nominal group technique about participation and commitment. An example of the use of NGT illustrates some of the issues to be raised here. An Example After months of frustration with apparently directionless and stalemated meetings, members of the local board of a regional transportation commission contacted the public management center at a nearby university for assistance. Several public administration faculty associated with the center sought to pinpoint the board's problems with in-depth interviews of board members and staff. The interviews revealed conflicts over a wide range of substantive and procedural issues facing the board. Some actors saw the source of their problems in poor administration, while others blamed the divergent political interests of the board members, who were also elected officials from various local jurisdictions. At the board's request, a conference in a retreat setting was organized to set priorities for policy objectives and to design concrete plans for action. Support for the conference was not unanimous, however,