广域组织内开放决策支持网格框架

针对政府机构和企业缩减规模的发展趋势,为地理上分布的管理人员进行更高效的跨应用领域决策支持提出一个5层决策支持框架,重点实现组织决策支持（Organization Decision Support,ODS）、提高决策质量,并提供一个公共操作环境以使组织在决策制定的整个生命周期中有效地进行信息协作．该框架以决策任务为主导,扩展传统的ODS框架,把不同的资源规范到一体的参考模型中,引入更多的与决策支持有关的协作模型,可为组织的辅助决策系统建设提供参考．     

The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making

The technique for order preference by similarity to ideal solution (TOPSIS) method is a well-known compromising method for multiple criteria decision analysis. This paper develops an extended TOPSIS method with an inclusion comparison approach for addressing multiple criteria group decision-making problems in the framework of interval-valued intuitionistic fuzzy sets. Considering the relative agreement degrees and the importance weights of multiple decision makers, this paper presents a modified hybrid averaging method with an inclusion-based ordered weighted averaging operation for forming a collective decision environment. Based on the main structure of the TOPSIS method, this paper utilizes the concept of inclusion comparison possibilities to propose a new index for an inclusion-based closeness coefficient for ranking the alternatives. Additionally, two optimization models are established to determine the criterion weights for addressing situations in which the preference information is completely unknown or incompletely known. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a medical group decision-making problem.     

Engaging multiple perspectives: A value-based decision-making model

In some decision contexts, such as a crisis or when confronted by a new or novel set of circumstances, people may be forced to make decisions with limited information or time available for analysis. In such contexts, the set of alternative solutions developed may be greatly affected by the personal values and the perspective those values precipitate. Being able to view the decision environment from multiple perspectives enhances the decision-maker's ability to make better-informed choices. This article introduces the value-based decision-making model that suggests that multiple perspectives may be achieved by considering a foundation of individual values. Empirical testing indicates that this model provides a viable framework that decision-makers and researchers can use to better understand and facilitate multiple perspectives in decision-making.     

Support for real-time decision making in mobile financial applications

Mobile users making real-time decisions based on current information need confidence that their context has been taken into consideration in producing the system’s recommendations. This chapter reviews current use of mobile technologies for context-aware real-time decision support. Specifically, it describes a framework for assessing the impact of mobility in decision making. The framework uses dynamic context model of data quality to represent uncertainties in the mobile decision-making environment. This framework can be used for developing visual interactive displays for communicating to the user relevant changes in data quality when working in mobile environments. As an illustration, this chapter proposes a real-time decision support procedure for on-the-spot assistance to the mobile consumer when choosing the best payment option to efficiently manage their budget. The proposed procedure is based on multi-attribute decision analysis, scenario reasoning, and a quality of data framework. The feasibility of the approach is demonstrated with a mobile decision-support system prototype implementation. This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde W. Holsapple (2008) Springer.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

Conflict resolution in a knowledge-based system using multiple attribute decision-making

The metarules useful in the conflict resolution are often directly related to the multiple, conflicting, and non-commensurate objectives associated with the problem domain. However, in its current form, the use of metarules for conflict resolution has some drawbacks. Above all, the metarule use in rule selection is not tailored to the current situation or the specific user; it is tailored to the domain expert, whose domain expertise and preferences were used to construct a knowledge-based system. In this paper, we presents a new method for resolving the conflicts of rules in the knowledge-based system, using decision analysis techniques that explicitly incorporate a user’s preference judgments about the rules. To this end, we consider a conflict resolution problem as a multiple attribute decision-making problem. Further, the proposed method allows for user’s preference judgments that are specified not by rigid format but by user-friendly format for the purpose of reducing burden of information specification.We have applied the proposed methodology to an organizational information-oriented service and resource planning, in which there exist multiple conflicting objectives to be considered.     

群决策中多阶段多元判断偏好的集结方法研究

研究群决策过程中决策者基干多个决策阶段、多种结构形式的判断偏好集结方法.基于互反判断偏好与互补判断偏好的转化公式,将多种类偏好的结构一致化;利用决策者判断偏好的一致性水平和与群体综合偏好偏差的距离,提出了确定决策者权重的方法;建立了基于决策先验信息的多阶段偏好集结的决策阶段赋权模型.根据各决策阶段的权重,将多阶段判断偏好集结成群体综合偏好.     

基于商务智能的黄山景区决策支持系统研究

黄山风景区经过多年的信息化建设,已初步建成了涉及景区多个领域的近30个应用系统。但是,这些系统之间彼此孤立,数据间难以实现共享,并且日益积累的大量历史数据没有得到充分利用。文中将商务智能技术运用到景区的业务经营管理中,通过利用ETL技术,解决各系统间的信息孤岛问题,并建设统一的数据仓库平台;在此基础上,根据景区日常业务经营管理需求,介绍构建基于商务智能技术的景区经营决策支持系统的框架、系统的决策功能以及关键技术分析。     

Coordination of purchasing and bidding activities across posted offer and auction markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-offer markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer’s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-offer markets with fixed transaction costs. The second set of models is concerned with the coordination of bidding activities across multiple online auctions.Research reported in this paper was partially supported by a Proposition 301 Electronic Commerce Grant, University of Arizona Eller College of Management. The first author is also affiliated with The Key Lab of Complex Systems and Intelligence Science, Chinese Academy of Sciences (CAS), Beijing, and was supported in part by a grant for open research projects (ORP-0303) from CAS. Earlier versions of this paper have appeared in the Proceedings of the Hawai’i International Conference on System Sciences (HICSS-37) and the Proceedings of the 13th Annual Workshop on Information Technology and Systems (WITS ’03).     

Information systems outsourcing decisions using a group decision-making approach

Outsourcing refers to a company that contracts with another company to provide services that might otherwise be performed by in-house employees. Information system (IS) outsourcing policies define the criteria that organizations utilize to decide upon the scope and degree of reliance of their IS capabilities upon external sources. IS outsourcing is an innovative organizational tool for IS management in both private and public sector organizations. In this paper, an interactive group decision-making methodology is proposed to select/rank IS providers under multiple criteria. A measure for the consensus level of the group preferences is developed to satisfy an acceptable level of group agreement and reliability. The Spearman coefficients for both the aggregated rank order and each DM's rank order have also been calculated. The group and the individual evaluations are gathered through a fuzzy TOPSIS approach. The proposed methodology is applied in the largest office furniture manufacturer in Konya-Turkey. Eight alternative IS providers are evaluated based on seven criteria by five decision makers. Sensitivity analyses are also provided to see the effects of parameter changes on the final decision.     

A recommender mechanism based on case-based reasoning

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.     

Group decision making problems in a linguistic and dynamic context

The aim of this paper is to present a new model of decision support system for group decision making problems based on a linguistic approach and dynamic sets of alternatives. The model incorporates a mechanism that allows to manage dynamic decision situations in which some information about the problem is not constant in time. We assume that the set of alternatives can change during the decision making process. The model is presented in a mobile and dynamic context where the experts’ preferences can be incomplete. The linguistic approach is used to represent both the experts’ preferences about the alternatives and the agreement degrees to manage the change of some alternatives. A prototype of such mobile decision support system in which the experts use mobile devices to provide their linguistic preferences at anytime and anywhere has been implemented. In such a way, we provide a new linguistic group decision making framework that is mobile and dynamic.     

基于SaaS模式的决策支持系统的研究

SaaS平台软件交付模式主要是把应用软件以服务的形式提供给用户,文中尝试探索了一种基于SaaS模式的决策支持系统的架构。主要方法是采用第三种成熟度模式来设计决策支持系统的架构,对于决策模型主要采用WebService来对服务决策模型进行封装。基于SaaS模式的决策支持系统就可以根据需求来绑定所需要的决策模型,通过实例来阐述模型绑定的关键步骤。分析了用户访问决策模型的具体流程,给出了使用决策模型的形式语言描述。     

On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty

In multiple attribute decision analysis (MADA), one often needs to deal with both numerical data and qualitative information with uncertainty. It is essential to properly represent and use uncertain information to conduct rational decision analysis. Based on a multilevel evaluation framework, an evidential reasoning (ER) approach has been developed for supporting such decision analysis, the kernel of which is an ER algorithm developed on the basis of the framework and the evidence combination rule of the Dempster-Shafer (D-S) theory. The approach has been applied to engineering design selection, organizational self-assessment, safety and risk assessment, and supplier assessment. In this paper, the fundamental features of the ER approach are investigated. New schemes for weight normalization and basic probability assignments are proposed. The original ER approach is further developed to enhance the process of aggregating attributes with uncertainty. Utility intervals are proposed to describe the impact of ignorance on decision analysis. Several properties of the new ER approach are explored, which lay the theoretical foundation of the ER approach. A numerical example of a motorcycle evaluation problem is examined using the ER approach. Computation steps and analysis results are provided in order to demonstrate its implementation process.     

Group decision making to better respond customer needs in software development

Quality Function Deployment (QFD) is a well-known planning methodology for translating customer needs into relevant design and production requirements. The intent of applying QFD is to incorporate the voice of the customer into the various phases of the product development cycle for a new product, or a new version of an existing product. The traditional QFD structure requires individuals to express their preferences in a restricted scale without exceptions. In practice, people contributing to the process tend generally to give information about their personal preferences in many different ways, numerically or linguistically, depending on their background. Moreover, collaborative decision-making is not an emphasized issue in QFD even though it requires several people's involvement. In this study, we extend the QFD methodology by introducing a new group decision making approach that takes into account multiple preference formats and fusing different expressions into one uniform group decision by means of fuzzy set theory. An application on software development is supplied to illustrate the approach.     

Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty

In many decision situations, it is inevitable to deal with both quantitative and qualitative information under uncertainty. Evidence-based reasoning within a multiple criteria decision analysis framework provides an alternative way of handling such information systematically and consistently. In this paper, the evidential reasoning (ER) approach is introduced, which is based on a recursive ER algorithm that, in essence, constitutes a nonlinear information aggregation process. To facilitate the application of the ER approach and as an indispensable part of its development, the nonlinear features of the ER information aggregation process need to be thoroughly investigated and properly understood. This forms the theme of this paper where the nonlinear features are explored by examining typical reasoning patterns in aggregating harmonic, quasi-harmonic, and contradictory decision information. This analytical investigation provides insights into the recursive nature of the ER approach as well as valuable experience that could be useful to other researchers and practitioners interested in developing and applying operation research/artificial intelligence (OR/AI)-based approaches for decision analysis under uncertainty. The analytical study is complemented by the numerical studies of two application examples. The analysis of a quality assessment problem for motor engines is aimed to show the step-by-step process of implementing the ER approach and to illustrate its nonlinear features in a real-life decision situation. The study of a more complex assessment problem in ship design is intended to demonstrate the potential of the ER approach and its supporting software for dealing with general decision problems.     

Linguistic intuitionistic fuzzy preference relations and their application to multi-criteria decision making

Linguistic intuitionistic fuzzy sets can be regarded as a qualitative form of intuitionistic fuzzy sets. This type of fuzzy sets uses a linguistic membership degree and a linguistic non-membership degree to represent the qualitative preferred and non-preferred judgments of decision makers. Preference relation is a useful and efficient tool for decision making that only requires the decision makers to compare two objects at one time. Taking the advantages of linguistic intuitionistic fuzzy sets and preference relations, this paper introduces linguistic intuitionistic fuzzy preference relations (LIFPRs) and studies their application to decision making. To ensure the ranking of objects reasonably, an additive consistency concept is introduced, and several of its desirable properties are discussed. To cope with inconsistent and incomplete LIFPRs, programming model-based methods to derive additively consistent LIFPRs and determine missing values are constructed, respectively. Subsequently, an approach to multi-criteria decision making with LIFPRs is offered, and the application of the new approach is illustrated by using a decision-making problem about evaluating mobile phones.     

Decision support systems: An expert systems approach

This paper provides a conceptual framework for designing decision support systems (DSS) using an expert systems approach. Currently there is a significant trend towards the use of knowledge-based systems techniques in DSS design, but a comprehensive framework is yet to be proposed. Our paper addresses this problem and presents such a framework. Efforts are currently underway to design, implement and test a system based on this framework.