A group decision support approach to evaluating journals

One of the most important decisions made in academic institutions, research organizations, and government agencies is the grading or ranking of journals for their academic values. Current methods for evaluating journals use either a subjective (e.g., experts’ judgments on journals) or objective approach (e.g., impact factors of journals), or an informal mix of the two. This paper presents a formal procedure that integrates objective and subjective judgments to provide a comprehensive method. The procedure is based on a fuzzy set approach that deals with the imprecise and missing information inherent in the evaluation process. The system was tested in Hong Kong in an assessment of faculty research productivity. Similar assessments exist in the UK, Singapore, and other countries. The proposed model can also be used for similar decisions that involve subjective and objective information.     

A Korean group decision support system

A Group Decision Support System (GDSS) can be used to improve communication in many languages. Heretofore, the vast majority of research with these systems has been conducted on American groups using English. Here, we demonstrate how groups of Korean students used a GDSS developed at the University of Mississippi to exchange comments in Korean and English anonymously and simultaneously. The study found no significant differences between the English and Korean systems in terms of self-assessed ratings of evaluation apprehension, production blocking, and process satisfaction. Participants rated both systems favourably, supporting our hypothesis that Korean groups can benefit from the use of a GDSS.     

A decision support system for evaluating intangible investments

In this research project we have connected the procedural and substantive decision support by means of modern information technology. We have showed how the semistructured strategic decisions concerning intangible investments can be supported effectively by relating appropriate analysis methods to the different phases of the investment process. Because for the intangible investments there is no well-defined solution procedure available, a variety of methods must be integrated to support the solution process.

As a demonstrative example we showed how the intangible investments in logistic systems can be managed. It was highlighted that the logistics systems are complex, cross-functional systems that affect all major functions or departments within the firm beginning from the raw material deliveries and ending with the deliveries of finished goods. Investments in the logistics systems have corporatewide, cross-functional effects, and the investment decisions are strategic decisions. Consequently, the investments in the logistics systems should be strategically justified, not only cost-justified. The cross-functional effects imply that the investment analysis concerning the whole logistics system must be fulfilled under multiple, diversified criteria. We can not, however, ignore the financial, cost and revenue oriented, traditional investment criteria. They just have to be used in a new, flexible way in relation to the goals and objectives of the firm.

Strategic planning is a managerial area where semistructured problems frequently exist. One of the key problems faced in strategic management is the issue of investment decisions. Investments in information systems, logistic systems, flexible production systems, corporate image, etc, are examples of complex investment problems that are difficult to quantify and analyze with traditional techniques. Modern computer technology can enrich the analysis and improve the quality of strategic decision making.     

A decision support tool for evaluating customer intentions

It is crucial for any manager to keep a close watch on customer satisfaction, customer loyalty and the customer’s intention to recommend the company. In this article, a new decision support tool is developed to support a manager with this task. This tool has been developed with companies in mind that posses limited customer satisfaction data. It uses model-based knowledge discovery to extract the customer’s expectation and the expectation-performance compatibility from the data. Two hypotheses are formulated which posit that compatibility between product performance and customer expectation have a positive influence on the customer’s intentions. Both hypotheses are supported by the data. Finally, a decision support tool is developed which visualizes the impact of customer satisfaction, product performance and expectation-performance compatibility on the customer’s intentions. The decision support tool contains three views which offer the manager important information at a glance.     

A Bayesian approach to model dispersal for decision support

In agricultural and environmental sciences dispersal models are often used for risk assessment to predict the risk associated with a given configuration and also to test scenarios that are likely to minimise those risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability and its prediction relies on models and parameter values which can only approximate the real processes. In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic data (distances between emitters and receptors; main wind direction) while accounting for uncertainty, with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in a non-GM field. This method includes the design of candidate models, their calibration, selection and evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be used for prediction purpose. The group of models allows to include local information and it reflects reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.     

A systematic approach to heterogeneous multiattribute group decision making

Heterogeneous multiattribute group decision making (MAGDM) problems which involve multi-granularity linguistic labels, fuzzy numbers, interval numbers and real numbers are very complex and important in practical applications of decision making theory. Hitherto, there exists no general theoretical inducement for solving such problems. The purpose of this paper is to develop a systematic methodology for solving the heterogeneous MAGDM problems by introducing the multiattribute ranking index based on the particular measure of closeness to the positive ideal solution (PIS) and using the weighted Minkowski distance to measure differences between each alternative and the PIS as well as the negative ideal solution (NIS). The proposed methodology is shown to have some advantages over the fuzzy TOPSIS. Validity and applicability of the methodology proposed in this paper is illustrated with a real example of the missile weapon system selection problem.     

A systematic approach to heterogeneous multiattribute group decision making

Heterogeneous multiattribute group decision making (MAGDM) problems which involve multi-granularity linguistic labels, fuzzy numbers, interval numbers and real numbers are very complex and important in practical applications of decision making theory. Hitherto, there exists no general theoretical inducement for solving such problems. The purpose of this paper is to develop a systematic methodology for solving the heterogeneous MAGDM problems by introducing the multiattribute ranking index based on the particular measure of closeness to the positive ideal solution (PIS) and using the weighted Minkowski distance to measure differences between each alternative and the PIS as well as the negative ideal solution (NIS). The proposed methodology is shown to have some advantages over the fuzzy TOPSIS. Validity and applicability of the methodology proposed in this paper is illustrated with a real example of the missile weapon system selection problem.     

Consensus-based decision support for multicriteria group decision making

Consensus decision making is complex and challenging in multicriteria group decision making due to the involvement of several decision makers, the presence of multiple, and often conflicting criteria, and the existence of subjectiveness and imprecision in the decision making process. To ensure effective decisions being made, the interest of all the decision makers usually represented by the degree of consensus in the decision making process has to be adequately considered. This paper presents a consensus-based approach for effectively solving the multicriteria group decision making problem. The subjectiveness and imprecision of the decision making process is adequately handled by using intuitionistic fuzzy numbers. An interactive algorithm is developed for consensus building in the group decision making process. A decision support system framework is presented for improving the effectiveness of the consensus building process. An example is presented for demonstrating the applicability of the proposed approach for solving the multicriteria group decision making problem in real world situations.     

A decision support approach to automatic timetabling in higher education institutions

At a time when the need to reduce costs has become part of the day-to-day reality of all educational institutions, it is unthinkable to continue to manually perform those tasks (i.e., the creation of timetables) that can be automated and optimized. The automatic creation of timetables for educational institutions is one of the most studied problems by the scientific community. However, almost all studies have been based on very simplified models of reality that have no practical application. A realistic model of the problem, robust algorithms that are able to find valid solutions in highly restricted environments, and optimization methods that are able to quickly provide quality results are key factors to consider when attempting to solve this (real) problem faced by educational institutions. This paper presents a summary of the work performed by Bullet Solutions over the last few years, from the first stage of understanding and modelling the problem to the final analysis of the results obtained using the developed software under real conditions.     

A parallel approach to the analytic hierarchy process decision support tool

The Multiple Criteria Decision Aiding methods dedicated to discrete problems follow different philosophies and strategies for selecting, clustering or ranking alternatives. This work presents a tool using one such method—the Analytic Hierarchy Process (AHP). The Decision Maker (DM) can structure his criteria as a hierarchy tree having the alternatives as leaf nodes. The DM must then build matrices for each node by performing pairwise comparisons between its children. The AHP finds the weights of each child concerning the parent criterion by calculating the elements of the eigenvector corresponding to the maximum eigenvalue of the comparison matrix. Weights are then combined in order to obtain the influence of each alternative on the top of the hierarchy. A DM expects that a Decision Support Tool works faster than he/she does. In order to achieve speed a parallel approach was developed. Parallel implementations described in this work follow different message-passing strategies and capitalise on the fact that the vector of weights for each matrix can be calculated independently. The authors used a network of four Inmos Transputers. Research will focus on finding which implementation will run faster and how the DMs options affect the speedups obtainable.     

Decider: A fuzzy multi-criteria group decision support system

Multi-criteria group decision making (MCGDM) aims to support preference-based decision over the available alternatives that are characterized by multiple criteria in a group. To increase the level of overall satisfaction for the final decision across the group and deal with uncertainty in decision process, a fuzzy MCGDM process (FMP) model is established in this study. This FMP model can also aggregate both subjective and objective information under multi-level hierarchies of criteria and evaluators. Based on the FMP model, a fuzzy MCGDM decision support system (called Decider) is developed, which can handle information expressed in linguistic terms, boolean values, as well as numeric values to assess and rank a set of alternatives within a group of decision makers. Real applications indicate that the presented FMP model and the Decider  software are able to effectively handle fuzziness in both subjective and objective information and support group decision-making under multi-level criteria with a higher level of satisfaction by decision makers.     

A multi-criteria decision support model for evaluating the performance of partnerships

Partnership is one of the strategies that could help companies increase their competiveness in a global market. Previous studies reported that a high percentage of partnerships fail to achieve their drivers of entering into partnership. The lack of a comprehensive partnership evaluation has been identified as one of the main reasons for partnership failure. In this paper, a multi-criteria decision support model is developed to evaluate the performance of an ongoing partnership in different periods based on the measures associated with the drivers for entering into the partnership. Interpretive Structural Modeling (ISM), Analytical Network Process (ANP) and Fuzzy Logic (FL) are used in order to address the interdependency, the importance of, and the uncertainty in performance measures, respectively. The outputs of the model are the importance of each performance measure and a single number for the overall partnership performance in each period, named as Partnership Performance Index (PPI) here. PPI is different from either mere financial or operational performance measures. PPI is a multi-dimensional measure which includes multiple performance measures associated with the partnership drivers and accounts for their importance and interdependencies. The model is applied to a partnership between a logging company and a sawmill in British Columbia, Canada. PPI is used to evaluate this partnership in three different periods. PPI values are compared to conventional measures for partnership evaluation and the managers confirmed that PPI values better represent the performance of their partnership. The sensitivity of the PPIs is investigated based on the changes in the importance as well as the value of the measures. The rankings from the model are compared to the ones estimated by the managers, and the results showed that the rankings are compatible. This model contributes to the literature by developing an index for partnership performance which captures partnership drivers and performance measures as well as their importance and interdependencies.     

Revisiting the supplier selection problem: An integrated approach for group decision support

Decision support for supplier selection is a highly researched theme in procurement management literature. However applications of group decision support theories are yet to be explored extensively in this domain. This study proposes an approach for group decision support for the supplier selection problem by integrating fuzzy Analytic Hierarchy Process (AHP) for group decision making and fuzzy goal programming for discriminant analysis. In the first step, the fuzzy AHP theory with the Geometric Mean Method has been used to prioritize and aggregate the preferences of a group of decision makers. Then consensus has been developed between these aggregated priorities using the Ordinal Consensus Improvement Approach. Subsequently, the consensual priorities of this group of decision makers have been integrated with fuzzy goal programming theory for discriminant analysis to provide predictive decision support. Finally it has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for group decision making using only AHP.     

A new approach to water quality modelling and environmental decision support systems

The concepts and technology of environmental decision support systems (EDSS) have developed considerably over recent decades, although core concepts such as flexibility and adaptability within a changing decision environment remain paramount. Much recent EDSS theory has focussed on model integration and re-use in decision support system (DSS) tools and for design and construction of ‘DSS generators’. Many current specific DSS have architectures, tools, models and operational characteristics that are either fixed or difficult to change in the face of changing management needs. This paper reports on development and deployment of an EDSS that encompasses a new approach to DSS tools, generators and specific DSS applications. The system, named E2, is built upon a conceptualisation of terrestrial and aquatic environmental systems that has resulted in a robust and flexible system architecture. The architecture provides a set of base classes to represent fundamental concepts, and which can be instantiated and combined to form DSS generators of varying complexity. A DSS generator is described within which system users are able to select and link models, data, analysis tools and reporting tools to create specific DSS for particular problems, and for which new models and tools can be created and, through software reflection (introspection), discovered to provide expanded capability where required. This system offers a new approach within which environmental systems can be described in the form of specific DSS at a scale and level of complexity suited to the problems and needs of decision makers.     

A decision support systems approach to adjust maintenance float system availability levels

In this paper, a decision support system (DSS) is designed to predict the system availability levels for equipment maintenance float problems. The DSS is developed by using the analytic hierarchy process (AHP) to prioritize the improvements made in the maintenance management practice through the adoption of total quality management (TQM). The resulting regression metamodel can be used to predict and explain system availability. Sensitivity analysis can be easily generated through the DSS. The application of this DSS helps to effectively control the cost of maintenance floats as a result of TQM implementation, generates quick solutions, and provides the decision maker with the flexibility of carrying out sensitivity analysis and planning for the future by looking at the long-term impacts of TQM on the maintenance float system.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

Social processes and group decision making: anonymity in group decision support systems

Various social processes in decision-making groups are considered detrimental to the quality of decisions. It is often assumed that removing the ability for groups to exert strong social influence on its members improves group decisions. Group Decision Support Systems (GDSSs) are increasingly used to remedy the social faults of the decision-making process in groups. In these systems, anonymity is seen as a tool to reduce the impact of the group over its members, and therefore as the key to improved group performance. This meta-analytic review examines the assumption that anonymity in GDSSs is beneficial for group decision-making on a range of performance indicators. In 6 meta-analyses of 12 independent investigations there is no support for this hypothesis. The only reliable effect of anonymity was to lead to more contributions, especially more critical ones. An alternative model is presented to account for the findings. This model argues that performance in decision-making groups depends on the social context and relevant social norms as well as on system characteristics such as anonymity. It is concluded that the integration of anonymity into phases of group decision support does not guarantee improved performance.     

A metalanguage design for sessions with group decision support systems

This paper proposes a metalanguage for representing communication among participants in a session of a group decision support system (GDSS). Such a vehicle can be used for setting standards and designing specific user interfaces with GDSS.Three different roles of participants are identified: chairperson, chauffeur and regular participant. The tasks and activities of each role are analyzed. Based on the analysis, communications requirements are derived. The requirements lead to a design of a metalanguage consisting of 15 different commands. The paper concludes by displaying the structure and syntax of each command.The work described here is conceptual and could be considered as a proposal for GDSS language design.     

An associative memory approach to medical decision support systems

Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets.