• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.249-254
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• 2009

The today's decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge's, decision making tools and possibly expertise's pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.195-218
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• 2005

The emergence of ubiquitous computing environment will change the service architecture of business information systems such as Decision Support System(DSS), which will be a new application. Recent mobile DSSs allow the decision makers to be benefited from web and mobile technology. However, they seldom refer to context data, which are useful for proactive decision support. Meanwhile, ubiquitous applications so far provide restricted personalization service using context and preference of the user, that is, they do not fully make use of decision making capabilities. Hence, this paper aims to describe how the decision making capability and context-aware computing are jointly used to establish ubiquitous applications. To do so, an amended DSS paradigm: CKDDM(Context-Knowledge-Dialogue-Data-Model) is proposed in this paper. What will be considered for the future decision support systems when we regard ubiquitous computing technology as an inevitable impact that enforces the change of the way of making decisions are described. Under the CKDDM paradigm, a framework of ubiquitous decision support systems(ubiDSS) is addressed with the description of the subsystems within. To show the feasibility of ubiDSS, a prototype system, CAMA-myOpt(Context-Aware Multi Agent System-My Optimization) has been implemented as an illustrative example system.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.34-47
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• 1988

Decision support system (DSS) has been expected to be a powerful tool for aiding the decision making processes in business organizations. But it's contribution has turned to be somewhat doubtful, In this paper, an intergrated systems design apporach is suggested, which integrates DSS and expert system (ES) for the enhancement of performance of DSS, after carefully reviewing both DSS and ES.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.139-151
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• 2007

Decision-making in the crisis management happens in dynamic, rapidly changing, and often unpredictable distributed environments. Crisis management Decision Support Systems (DSSs) in E-Government are challenged by the need to use it availably at anytime, from anywhere, and even under any-situation. In this paper the reasons of developing distributed architecture for crisis management Decision Support Systems (DSSs) in E-Government are analyzed. Consequently, a distributed architecture for crisis management Decision Support System (DSS) is proposed in this paper. Finally it is implemented by Web Services. If crisis management Decision Support System (DSS) based on distributed architecture is implemented by Web Service, then it can provide decision support for decision-makers to deal with crisis at anytime, from anywhere, and even under any-situation.

• Korean Management Science Review
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• v.9 no.1
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• pp.53-65
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• 1992

Decision Support System(DSS) is an advanced information system concept prevailing since 1980s. It supports managers' decision making activities by providing not only information but decision alternatives. It essentially consists of the components of database, modelbase and dialogue systems. With the development of its databases, POSCO needs a number of modelbases in establishing DSSs in various areas such as production planning and investment. This research particularly focuses on establishment of a mid-and long-range investment DSS and investigates the necessity and the problems of an investment DSS and the decision criteria for investment priority. We (1) propose a modelbase which uses the concepts of Analytic Hierarchy Process(AHP) and Zero-Base Budgeting(ZBB), along with an appropriate scoring method, database and dialogue system to support the investment manager in evaluating investment proposals ; (2) implement the system using relational database management system ; and (3) discuss the results of implementing the investment DSS.

• Water Engineering Research
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• v.5 no.4
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• pp.157-176
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• 2004

This research presents a prototype development and implementation of Decision Support System (DSS) for integrated river basin water management for the flood control. The DSS consists of Relational Database Management System, Hydrologic Data Monitoring System, Spatial Analysis Module, Spatial and Temporal Analysis for Rainfall Event Tool, Flood Forecasting Module, Real-Time Operation of Multi Reservoir System, and Dialog Module with Graphical User Interface and Graphic Display Systems. The developed DSS provides an automated process of alternative evaluation and selection within a flexible, fully integrated, interactive, centered relational database management system in a user-friendly computer environment. The river basin decision-maker for the flood control should expect that she or he could manage the flood events more effectively by fully grasping the hydrologic situation throughout the basin.

• Smart Structures and Systems
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• v.26 no.4
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• pp.533-544
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• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.214-217
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• 1996

Early decision support systems (DSS) were the "passive" decision support systems in the sense that the systems only able to do what the users explicitly direct them to do. But some researchers such as Raghav Rao et al. [51 showed architectures to suggest general idea of the innovative DSS systems which offer active form of decision support, say, "active Intelligent Decision Support Systems(active IDSS)". The system can perform not only what the users want to do but some voluntary (or involuntary) intelligent works. This paper presents the issues in the design of the active IDSS in the domain of investment evaluation, a domain area where few researchers have suggested frameworks or architectures to discriminate good investment from bad one. We propose a new paradigm, by utilizing historical investment results using neural network and Multivariate Discriminant Analysis(MDA), to identify goodness of investment. A new active IDSS architecture which consists of neural network, expert system and three components of the traditional passive DSS is suggested with some scenario based results.nario based results.

• IE interfaces
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• v.2 no.1
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• pp.47-58
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• 1989

Many decision problems in the real world have uncertainty and complexity. In many cases, decision makers do not have decision-analytic knowledge enough to solve a given decision problem. This paper developes a Decision Support System(DSS) that can be used for structuring decision problem into decision tree based on the concept of influence diagram and analyzing the decision problem by following Decision Analysis Cycle. This study suggests a DSS system(DACUL) in order to implement Decision Analysis Cycle using Lotus1-2-3. DACUL system has been developed in IBM XT/AT compatible PC.

• The Research Journal of the Costume Culture
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• v.17 no.4
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• pp.723-732
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• 2009

The quick response(QR) system is very popular in Korean apparel companies. However, the usage of QR system was not known well. The purpose of this study is to identify the usage of the quick response decision support system(QR DSS) and postponement manufacturing in the Korean apparel company. The researched company was the only one which used the QR DSS. The researchers carried out the depth interview with the QR decision makers of the company. This company had 14 brands, and had used the QR DSS since January, 2008. The results are as follows: The QR DSS was supportive computer software program, and it helped the staffs to make agile decision about QR repeat production of clothing. The QR DSS automatically calculated the related data, and suggested the expected sales volume and the proper supply amounts of the styles. There were four functions in QR DSS : 'QR Alert', 'Proper Supply Amount Simulation', 'Sensible QR', and 'Supply/Sales Simulation by Item'. The men's clothing brands effectively used 'Supply/Sales Simulation by Item' function. And the women's clothing brands effectively used 'QR Alert' function. This company also used the postponement production system for QR repeat production. The postponement production was conducted with four methods : the yarn stocking, the grey fabric stocking, the dyed fabric stocking, and the fabric sourcing. The men's clothing brands usually used of the yarn stocking methods and the dyed fabric stocking methods. The women's clothing brands usually used the grey fabric stocking methods. By using QR DSS and postponement production system the company was able to shorten the lead time for QR decision making.