Continuous Ordered Weighted Distance Measure and Its Application to Multiple Attribute Group Decision Making

We develop a new distance measure called the continuous ordered weighted distance (COWD) measure by using the continuous ordered weighted averaging (COWA) operator in the interval distance. We study some properties and different families of the COWD measure. We further generalize the COWD measure. The prominent characteristics of the COWD measure are that it is not only a generalization of some widely used distance measures and the continuous generalized OWA operator, but also it can deal with interval deviations in aggregation on interval arguments by using a controlled parameter. The desirable characteristics make the COWD measure be suitable to wide range situations, such as decision making, engineering and economics. In the end, we develop the new approach to group decision making in investment selection.     

A Note on Linguistic Hybrid Arithmetic Averaging Operator in Multiple Attribute Group Decision Making with Linguistic Information

In this paper, we propose a linguistic hybrid arithmetic averaging (LHAA) operator, which is based on linguistic weighted arithmetic averaging (LWAA) operator and extended ordered weighted averaging (EOWA) operator, and study some desirable properties of the LHAA operator. The LHAA operator can not only reflect the importance degrees of both the given argument and its ordered position, but also relieve the influence of unfair arguments on the decision results by weighting these arguments with small values. Based on the LWAA and LHAA operators, we develop a practical approach to multiple attribute group decision making under linguistic environment. The approach first aggregates the individual linguistic preference values into a collective linguistic preference value for each alternative by using the LWAA and LHAA operators (it is worth pointing out that the aggregation process does not produce any loss of linguistic information), and then orders the collective linguistic preference values to obtain the best alternative(s). Finally, an illustrative example is also given to verify the approach and to demonstrate its feasibility and practicality.     

Coalition Analysis in Group Decision Support

GMCR II, a decision support system based on the Graph Model for Conflict Resolution, now contains an algorithm for Coalition Analysis to alert users that certain decision-makers would find it both feasible and beneficial to co-ordinate their actions. The theory and implementation of Coalition Analysis in GMCR II are discussed and illustrated.     

Generalized Multiple Averaging Operators and their Applications to Group Decision Making

In this paper, we present a new operator called the generalized ordered weighted multiple averaging (GOWMA) operator based on a minimization problem with penalty function, which unifies the geometric mean and the generalized ordered weighted averaging operator with the generalized ordered weighted harmonic averaging operator in the same formulation. We study different properties and families of the GOWMA operator and develop a generalization of this operator called the generalized hybrid multiple averaging operator. To determine the GOWMA operator weights, we propose the generalized least squares method which does not follow a regular distribution. Finally, we give a numerical example of an investment selection to illustrate the application of the GOWMA operator to multiple attribute group decision making.     

Multiple Criteria Decision Making Models in Group Decision Support

Use of multiple criteria decision making (MCDM) models to aid the group decision process was tested. Two multiple criteria group decision support systems (MCGDSS) were studied, one using the AHP/Tchebycheff method of Iz and the other using Kersten's NEGO system. These systems were compared with a commercial GDSS, VisionQuest. VisionQuest does not include multiple criteria tools. To make the study comparable, VisionQuest was augmented with an ad hoc linear programming model that could generate solutions with specified characteristics requested by the using group. The three systems were compared on the dimensions of solution quality and decision support effectiveness.One of the hypotheses was that MCDM models would force participants to examine criteria, preferences, and aspirations more thoroughly, thus leading to decisions of better quality. Subjects using the MCGDSSs were expected to have higher mean quality and effectiveness values. However, the quality and effectiveness values of the VisionQuest/ad hoc system were found to be better on the dimension of effectiveness. Explanations for this result are included in the paper.Another hypothesis was that the AHP/Tchebycheff method of Iz, a value-oriented system, would yield more effective group support than the goal-oriented NEGO system. However, the NEGO system was found to yield solutions with better quality measures than the solutions obtained with the AHP/Tchebycheff system.Observation of the groups using the MCDM systems indicate that both the AHP/Tchebycheff and NEGO methods can be revised to enhance their effectiveness. The primary difficulty encountered with the AHP/Tchebycheff method was in the large number of pairwise comparisons required by AHP. The NEGO method can be enhanced by including specification of desired attainment levels in the first stage of the method. Both MCDM techniques have potential to benefit group decision support by giving using groups a means to design better solutions.     

Developing a Group Decision Support System for Advertising Media Evaluation: A Case in the Middle East

The importance of advertising media evaluation as a multifaceted problem is well known by both academics and practitioners. Although previous studies tried to optimize media evaluation, there still are some gaps and problems to address, particularly in areas of flexibility of models/frameworks, decision making quality, tension management, and agility of the evaluation process. Most of previous studies are based on inflexible models/frameworks that have limitations on number of criteria/alternatives they can consider and type of data they can process. A great volume of the work used arbitrary decision making; arbitrary decision making regarding criteria and media importance may reduce effectiveness of advertising campaigns. Furthermore, the academic literature offers little guidance on group decision aggregation, and tension management during decision making is neglected. Media evaluation is a time taking process and any acceleration will reduce pre-campaign costs. The main aim of this paper is to illustrate how a group decision support system (GDSS) can assist media planners to overcome mentioned problems more systematically. For this purpose, we developed a GDSS that is an integration of three well-known multi-criteria decision making techniques. With a real world case study, we illustrate the performance of the proposed GDSS. Results of our quantitative assessments indicate that the GDSS is flexible, allows decision makers to express their opinions, reduces tension among decision makers, and saves time.     

Monte Carlo Simulation Techniques in a Decision Support System for Group Decision Making

This paper describes a group decision support system based on an additive multi-attribute utility model for identifying a consensus strategy in group decision-making problems where several decision-makers or groups of decision-makers elicit their own preferences separately. On the one hand, the system provides procedures to quantify the DMs or group of DMs preferences separately. This involves assessing the DMs or group of DMs component utilities that represent their preferences regarding the respective possible attribute values and objective weights that represent the relative importance of the criteria. On the other hand, we propose Monte Carlo simulation techniques for identifying a consensus strategy. An iterative process will be carried out, where, after the simulations have been performed, the imprecise component utilities and weights corresponding to the different DMs or groups of DMs are tightened to output more meaningful information in the next simulations to achieve a consensus strategy. Finally, an application to the evaluation of remedial strategies for restoring contaminated aquatic ecosystems illustrates the usefulness and flexibility of this decision support tool.     

Deriving Weights from Interval Multiplicative Preference Relations in Group Decision Making

In this article, we investigate group decision making problems with interval multiplicative preference relations (including complete interval multiplicative preference relations and incomplete interval multiplicative preference relations). On the basis of the number of judgments and the consistency degree of each interval multiplicative preference relation, we first give a combined weighting method to derive the weights of decision makers. Then, we establish two linear programming models to derive the weight intervals of alternatives from all individual consistent interval multiplicative preference relations and utilize the continuous ordered weighted averaging operator or the continuous ordered weighted geometric operator to aggregate all the values in each weight interval. In addition, we establish a more general model to check the consistency of all individual interval multiplicative preference relations. In the cases where the optimal objective value of the model is not zero, we can get the optimal weights of alternatives directly, and then utilize these optimal weights and the optimal deviation values derived from the model to construct consistent interval multiplicative preference relations. Furthermore, we discuss some special cases of the established models and illustrate our models with a practical example.     

Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.     

Influence Allocation Methods in Group Decision Support Systems

Influence allocation processes are voting and opinion aggregating methods that allow members to distribute some or all of their decision making influence to others in the group in order to exploit not only the group's knowledge of the alternatives, but its knowledge of itself. Only with the common use of group decision support systems (GDSS) has their use become practical. In this paper we reconsider SPAN, an influence allocation process introduced by MacKinnon (1966a). Experimental comparison shows SPAN to be significantly better at selecting a correct option from a set of options than two common voting methods. An alternative influence allocation process that we call RCON (Rational Consensus), is based on a weighting method proposed by DeGroot (1974) and has been explicated as a normative standard for combining opinion by Lehrer and Wagner (1981). The judgmental inputs to SPAN would appear to be logically related to those for RCON. Submitting the SPAN inputs from the experiment, transformed in this logical way, to the RCON process results in somewhat better performance than with SPAN. However, evidence indicated that the two methods are conceptually and psychologically sufficiently different that an experimental comparison is needed between them.     

Internet Multiattribute Group Decision Support in Electronic Commerce

The rapid growth of the Internet has provided the means for distributed organizational decision making for electronic commerce. Members of organizations can jointly investigate products, exchange information, and make decisions on-line from remote sites. Internet-based multiattribute group decision making is characterized by three aspects, (i) individual interactive decision making, (ii) communication means, and (iii) group consensus reaching. The purpose of this research was to study the role of communication and individual decision strategies and their influence on multiattribute group decision making and consensus reaching in organizational electronic commerce settings. The results of this study indicate that analytic decision support is indispensable in collaborative Internet-based decision making, that a perfect match of analytic decision support and communication channels must be achieved, and that efficiency of individual decision support should be compromised for higher confidence in the group's decisions. The results of this study also confirm findings by Häubl and Trifts (2000) that interactive decision analytic support has positive effects on the quality and efficiency of individual decision making, and findings by Limayem and DeSanctis (2000) and Todd and Benbesat (2000), that decision makers will use normative decision models if they require little effort and if decisional guidance is provided. The conclusion drawn from this study is that the continuously evolving Internet technology for collaborative decision making is only one aspect for better organizational decision making – the crucial aspect, however, will be the development and optimal integration of analytic decision models, communication channels, and consensus reaching mechanisms.     

An Integrated Group Decision Making Support Model for Corporate Financing Decisions

The essential goal of corporate finance is to maximize corporate value while reducing a firm’s financial risks. Corporate financing decision is a kind of multi-criteria based group decision making that embodies major approaches to handle qualitative criteria and quantitative limitations. However, in literature related to financing decision making, very little research uses decision making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) methods to consider the impact and dependency of its factors, or uses Goal programming (GP) to find the satisfactory financing decision under the related financial constraints. This study proposes an integrated group decision making support (GDMS) model to assist corporate financing group decision makers (DMs) in obtaining a satisfactory group solution. ANP, DEMATEL and GP are combined in this GDMS model. By using this model, the group DMs can systemically structure a multi-criteria network framework and derive priority weights of those criteria, and then deal with the quantitative financial constraints for a satisfactory group solution. An illustrative case is demonstrated for the effectiveness and practicability of this GDMS model.     

The Continuous Quasi-OWA Operator and its Application to Group Decision Making

In this paper, we extend the Quasi-OWA operator to the case in which the input argument is a continuous valued interval and present the continuous Quasi-OWA (C-QOWA) operator, which generalizes a wide range of continuous operators such as the continuous ordered weighted averaging (C-OWA) operator, the continuous generalized OWA operator (C-GOWA) and the continuous generalized ordered weighted logarithm aggregation (C-GOWLA) operator. Then an orness measure to reflect the or-like degree of the C-QOWA operator is proposed. Moreover, some desirable properties of the C-QOWA operator associated with its orness measure are investigated. In addition, we apply the C-QOWA operator to the aggregation of multiple interval arguments and obtain the weighted C-QOWA operator, the ordered weighted C-QOWA (OWC-QOWA) operator, the combined C-QOWA (CC-QOWA) operator. Finally, a CC-QOWA operator-based approach for multi-attribute group decision making problem is presented, and a numerical example shows that the developed approach is feasible and the results are credible.     

Generalized Ordered Weighted Reference Dependent Utility Aggregation Operators and Their Applications to Group Decision-Making

To quantify the influence of decision makers’ psychological factors on the group decision process, this paper develops a new class of aggregation operators based on reference-dependent utility functions (RUs) in multi-attribute group decision analysis. RUs include S-shaped RU and non-S-shaped RU. Each RU affords a framework where the psychological factors explicitly enter the decision problem via the basic utility function, reference point and loss aversion coefficient. Under the general framework, we derive a generalized ordered weighted S-shaped RU proportional averaging (GOSP) operator and a generalized ordered weighted non-S-shaped RU proportional averaging (GONSP) operator, respectively. The GOSP operator implies the risk attitude of the DM for relative losses is risk-seeking, while GONSP operator indicates the risk attitude in this case is risk-averse. As a special case, GONSP operator can degenerate into GOWPA operator which means that the attitude of the DM is risk-neutral. Each operator satisfies the desirable properties of general operator, i.e., monotonicity, commutativity, idempotency and boundedness. Furthermore, we consider hyperbolic absolute risk aversion (HARA) function as the basic utility function, and define an S-shaped HARA and a non-S-shaped HARA utility functions. Based on the two new RUs, we propose GOSP–HARA operator and GONSP–HARA operator. Every operator covers many existing aggregation operators. To ascertain weights of such operators, the paper builds an attribute-deviation weight model and a DMs-deviation weight model. Based on these RU operators and weight models, an approach is addressed for solving multiple attribute group decision-making problem. At last, an example is provided to show the feasible of our approach.     

A framework for thinking about Group Decision Support Systems (GDSS)

Strategic problem solving in organizations is a social process that disturbs established social relationships. Maintaining a negotiated social order is crucial to political feasibility and to emotional commitment from the participants in relation to a solution package. However, Group Decision Support Systems that attend overly to managing social order risk group think through bounded vision. This may be avoided if emotional commitment is also encouraged through participants experiencing the problem situation from multiple perspectives and in relation to alternative solution strategies. Commitment depends upon both means/ends rationality and procedural rationality. This acknowledges the balance in providing support to a group with respect to the negotiation of social order, with the more traditional group decision support for socially negotiating order out of the problem situation. This article argues that effective Group Decision Support Systems must attend to both aspects of creating order. OR modelling methods and the support that can be provided by modern micro-computers offer a new way forward—models can be toys that a group can play with together, enabling them to create knowledge as well as use it.     

Challenging Group Support Systems Research: The Case for Strategic Decision Making

Strategic decision making (SDM) often occurs in groups that can benefit from the use of group support systems (GSS). However, no comprehensive review of this logical intersection has been made. We explore this intersection by viewing GSS research through the lens of SDM. First, SDM is broadly characterized and a model of GSS-mediated SDM is produced. Second, we review empirical GSS research linking these findings to the characteristics of SDM. We conclude that GSS research has not produced sufficient knowledge about group history, heterogeneity, member experience, task type, time pressure, technology or tool effects, and decision consensus for a favorable evaluation of SDM in GSS groups. SDM in GSS groups challenges researchers to study the effects of group processes such as those just mentioned in a context that involves ongoing and established groups, political activity, and a multiplicity of tasks.     

Contrasting Single User and Networked Group Decision Support Systems for Strategy Making

The use of computers to support group work – as a Group Decision Support System (GDSS) – on strategy making has grown over the last decade. Some GDSS's have a facilitator managing the computer with the group viewing a public screen displaying the debate, problem definition, and agreements of the group as it negotiates strategies. Others involve members of the group in the direct input of data that forms part of the problem definition – data that is then used by the group employing electronic voting and other organizing devices. This paper discusses a real case relating to an organization seeking to reach important agreements about its strategy. The case involved the top management team and over 50 senior managers. The organization used a facilitator driven GDSS for some of this work, and a networked system for other parts. Some of the meetings were video taped, some were observed through one-way mirrors, and all of the participants were interviewed about their reactions to the different systems. This paper reports on some of the significant contrasts between the two approaches.     

A New Linear Programming Method for Weights Generation and Group Decision Making in the Analytic Hierarchy Process

This paper proposes a new linear programming method entitled by LP-GW-AHP for weights generation in analytic hierarchy process (AHP) which employs concepts from data envelopment analysis. We propose four specially constructed linear programming (LP) models which are used to derive weight vector from a pair-wise comparison matrix or a group of them. We can use both interval and relative importance weights for each decision maker in LP-GW-AHP. In this method, solving only one LP model is enough for local weights derivation from pair-wise comparison matrices. Five numerical examples are examined to illustrate the potential applications of the LP-GW-AHP method. We show that not only derived weights of the new method have slight differences than Saaty’s eigenvector weights but sometimes they are better than eigenvector method weights in the fitting performance index as well. LP-GW-AHP is compared with a method which has been recently proposed for the weights derivation in the group AHP and it becomes obvious that LP-GW-AHP provides better weights. The simple additive weighting method is utilized to aggregate local weights without the need to normalize them.     

PROMETHEE Group Decision Support System and the House of Quality

Quality function deployment (QFD) is a multi-step method that monitors customer needs throughout a product development process. The House of Quality (HOQ) exercise undertaken in the first phase of QFD is considered as the most important, since customer needs must be accurately translated into a set of technical requirements for the final product. This paper provides a PROMETHEE group decision support system (GDSS) approach that integrates the design preferences of the QFD team. We highlight the selection and ranking of the technical requirements in the HOQ exercise, where a group of multidisciplinary decision makers (DMs) in a globally dispersed QFD team is required to input their individual preferences. Our approach advances the HOQ group decision making context in three important areas. First, it treats each criterion and DM as unique in terms of the preference function and threshold levels. Second, it seeks a multi-criteria approach for the HOQ process, where some DMs may play a more important role than others on a certain criterion. Third, sensitivity analysis through the Geometrical Analysis for Interactive Assistance (GAIA) plane provides valuable information about the conflicts, similarities, or independencies between the criterion and the DMs, respectively. A case on an automotive part illustrates the performance of the PROMOTHEE approach with GAIA.     

Some Intuitionistic Fuzzy Weighted Distance Measures and Their Application to Group Decision Making

Xu and Chen (J Syst Sci Syst Eng 17:432–445, 2008) introduced a new decision-making technique called the ordered weighted distance (OWD) measure, having been proved useful for the treatment of situation where the available information is represented in exact numerical values. In this paper, we consider the situations with intuitionistic fuzzy and interval-valued intuitionistic information, and develop some intuitionistic fuzzy weighted distance measures such as intuitionistic fuzzy ordered weighted distance (IFOWD) measure, interval-valued intuitionistic fuzzy ordered weighted distance (IVIFOWD) measure, intuitionistic fuzzy hybrid weighted distance (IFHWD) measure and interval-valued intuitionistic fuzzy hybrid weighted distance (IVIFHWD) measure. These developed weighted distance measures are very suitable to deal with the situation where the input data are represented in intuitionistic fuzzy numbers or interval-lvalued intuitionistic fuzzy numbers. Then we present a consensus reaching process for group decision making with intuitionistic fuzzy preference information based on the developed distance measures. Finally, a practical application of he developed approach to the problem of evaluating university faculty for tenure and promotion is given.