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.     

Computer-based support for clinical decision making

Although computers are now commonly used for financial purposes in hospitals and physicians' offices, most physicians do not routinely use them in patient care. And in hospitals where laboratory data are provided on computer terminals, the displays are often difficult to use and programs that offer assistance in interpreting the data are usually unavailable. We have developed decision support programs that are widely used with the clinical computing system at our hospital. This paper describes the programs and how the clinicians use them.     

Scenario modelling to support industry strategic planning and decision making

The Pastoral Properties Futures Simulator (PPFS) is a dynamic systems model, developed within a participatory action research partnership with the pastoral industry of Australia's Northern Territory. The model was purpose-built to support the industry's strategic planning capacity in the face of environmental, market and institutional uncertainty. The mediated modelling process sought to maximise social learning of industry stakeholders. Simulations were conducted using scenarios representing combinations of climatic, market, institutional and technological assumptions. Stochastic parameters included rainfall and product prices. Economic and environmental performance of model farms, including greenhouse gas emissions, were estimated. A critical evaluation of the tool finds the PPFS fit for purpose. However, limitations include lack of output validation, small number of scenarios and simplistic treatment of environmental impact dimensions. With further development, the PPFS can provide a platform (a) to assist with industry planning across the whole of Northern Australia and beyond, and (b) for policy analysis and development in the context of the Australian pastoral industry.     

The study of indexing techniques on object oriented databases

An object-oriented database (OODB) has been becoming more important in recent years. It can deal with a large amount of complex objects and relationships that relational database (RDB) systems cannot handle well. However, the retrieval and update performance of an OODB depends on indexing techniques. In this paper, we study the indexing techniques on OODBs, based on an inheritance hierarchy and an aggregation hierarchy. Given the access probability and the size of each class, we propose a cost function to evaluate the gain of building an index on an inheritance hierarchy. For an aggregation hierarchy, we use a path-catenation technique to evaluate how to build index files on classes. Through some experiments, we found our methods have better retrieval performance than most ones proposed before.     

User responsibility and exception handling in decision support systems

The role of user responsibility in decision support systems (DSS) has been omitted in recent studies of some important issues. One such important issue is exception handling. In this short note, the role of user responsibility in exception handling is examined. The importance of user responsibility is emphasized, a possible approach is outlined which supports user responsibility in exception handling through the consideration of mental models.     

The situation categorization for support decision making

The estimation to situations in task of computer support of problems decision making by means of the categorizations mechanism of observed variable measurements or estimation collections is offered in article. The article is published in the original. Vyacheslav Leonidovich Tokarev. Born in 1942. Graduated from the Tula Polytechnic Institute (the city of Tula) in 1965 on specialty “Automatics and Telemechanics.” Defended a Ph.D. thesis in 1981 and doctoral thesis in 2000. Works at the Tula State University as a professor at the department of electronic computers. Circle of scientific interests: intelligent decision-making support systems, the development and application of the methods of the pattern recognition and image analysis theory for solving complex practical tasks. Two monographs and more than 20 articles on the results of the scientific activity were published. Member of the RAROAI (the Russian Association of Pattern Recognition and Image Analysis).     

Coordination policies to support decision making in distributed production planning

Distributed production networks are considered organizational structures able to match agility and efficiency necessary to compete in the global market. Performances of such organization structures heavily depend on the ability of the network actors of coordinating their activities. The research proposes to model and design coordination problems within production network by using the Multiple Agent Technology. In particular, the paper proposes new strategies for coordinating production-planning activities within production networks. Such models have been developed and tested by using a proper simulation environment developed by using open source code and architecture.

The results of the research can be located at two levels: (a) concerning the specific coordination problem addressed, the research provides some insights to make decisions about the choice of coordination approaches to be used in distributed production planning problems; (b) at more strategic level, the paper shows how Agent Technology and discrete event simulation can be used to build up efficient coordination structures for production networks.     

A visualization tool to support decision making in environmental and biological planning

Large-scale ecosystem management involves consideration of many factors for informed decision making. The EverVIEW Data Viewer is a cross-platform desktop decision support tool to help decision makers compare simulation model outputs from competing plans for restoring Florida's Greater Everglades. The integration of NetCDF metadata conventions into EverVIEW allows end-users from multiple institutions within and beyond the Everglades restoration community to share information and tools. Our development process incorporates continuous interaction with targeted end-users for increased likelihood of adoption. One of EverVIEW's signature features is side-by-side map panels, which can be used to simultaneously compare species or habitat impacts from alternative restoration plans. Other features include examination of potential restoration plan impacts across multiple geographic or tabular displays, and animation through time. As a result of an iterative, standards-driven approach, EverVIEW is relevant to large-scale planning beyond Florida, and is used in multiple biological planning efforts in the United States.     

ExPlanTech: multiagent support for manufacturing decision making

ExPlanTech's multiagent approach offers a unified framework for decision-making support and provides a proven alternative to known mathematical and system science-modeling technologies for simulating the manufacturing process. ExPlanTech provides technological support for various manufacturing problems and comprises different components, which you can assemble to develop a customized system that supports a user's decision making in different aspects of production planning. The system should help human users size resources and time requirements for a particular order, creating production plans, optimizing material resources manipulation, managing and optimizing supply chain relationships, visualizing and analyzing medium- and long-term manufacturing processes, and accessing external data.     

基于数据仓库技术的CRM决策支持系统模型设计

针对目前企业决策支持系统面临的新问题,介绍了CRM相关知识和决策支持系统的前沿技术——数据仓库及数据挖掘和联机分析处理,并在此基础上,提出了以数据仓库为中心、数据挖掘和联机分析处理为手段的面向客户关系管理的决策支持系统模型框架,描述了CRM数据运作流程和数据仓库等技术在其中所起的重要作用。     

Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making

Considering that there may exist some interactions between membership function and non-membership function of different intuitionistic fuzzy sets, we present some new operational laws from the probability point of view and give a geometric interpretation of the new operations. Based on which, a new class of generalized intuitionistic fuzzy aggregation operators are developed, including the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator, the generalized intuitionistic fuzzy ordered weighted geometric interaction averaging (GIFOWGIA) operator and the generalized intuitionistic fuzzy hybrid geometric interaction averaging (GIFHGIA) operator. The properties of these new generalized aggregation operators are investigated. Moreover, approaches to multiple attributes decision making are given based on the generalized aggregation operators under intuitionistic fuzzy environment, and an example is illustrated to show the validity and feasibility of new approach. Finally, we give a systematic comparison between the work of this paper and that of other papers.     

A consistency and consensus based decision support model for group decision making with multiplicative preference relations

In group decision making (GDM) with multiplicative preference relations (also known as pairwise comparison matrices in the Analytical Hierarchy Process), to come to a meaningful and reliable solution, it is preferable to consider individual consistency and group consensus in the decision process. This paper provides a decision support model to aid the group consensus process while keeping an acceptable individual consistency for each decision maker. The concept of an individual consistency index and a group consensus index is introduced based on the Hadamard product of two matrices. Two algorithms are presented in the designed support model. The first algorithm is utilized to convert an unacceptable preference relation to an acceptable one. The second algorithm is designed to assist the group in achieving a predefined consensus level. The main characteristics of our model are that: (1) it is independent of the prioritization method used in the consensus process; (2) it ensures that each individual multiplicative preference relation is of acceptable consistency when the predefined consensus level is achieved. Finally, some numerical examples are given to verify the effectiveness of our model.     

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.     

Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making

The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results.     

Visualization techniques for mining large databases: a comparison

Visual data mining techniques have proven to be of high value in exploratory data analysis, and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick-figure visualization techniques. For the evaluation of visual data mining techniques, the perception of data properties counts most, while the CPU time and the number of secondary storage accesses are only of secondary importance. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques     

Adaptive consensus support model for group decision making systems

In this paper, we present an adaptive consensus support model for group decision making systems based on intervals of linguistic 2-tuples. The proposed method has the following advantages: (1) the evaluating values can either be represented by linguistic terms or intervals of linguistic terms, (2) if the required consensus degree is too high, then the proposed adaptive consensus support model can modify experts’ preferences to improve convergence toward a higher consensus degree or a sufficient agreement for group decision making and (3) the proposed method is an interactive method, where each expert can modify the adjustments made by the system during the consensus reaching process if he/she does not agree with the adjustments made by the system. The proposed adaptive consensus support model can overcome the drawback of Mata et al.’s method (2009). It provides us with a useful way for adaptive consensus support for group decision making based on intervals of linguistic 2-tuples.     

System of decision making support based on fuzzy models

The scheme of an integrated model for decision making support in ill-structured situations is described using fuzzy expert estimates based on a hierarchic model. This integrated model makes it possible to rank the set of alternative solutions with respect to the set of criteria, the importance of which is determined relative to the general aim in the resulting hierarchic model of situation. The program system for the model construction and ranking is described.     

直觉模糊Power交叉影响算子及其应用

将Power算子推广到直觉模糊环境中,提出了直觉模糊Power交叉影响平均算子,考虑了不同直觉模糊集的隶属度与非隶属度之间可能存在的交叉影响,根据数学归纳法得到了其具体计算公式,并研究了其性质。实例说明了新的集成算子在多属性群决策应用中的有效性。将提出的算子与现存的直觉模糊Power平均算子做了稳定性比较。