• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.435-440
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• 2004

Most of our GIS implementation activities have been focussed on the management of urban information in local municipalities. Management of urban facilities has been the major issue of concern and has little role in providing the decision maker with alternatives from which one can analyse and choose the optimum solution. For this reason, the spatial decision support system is in need. Business analysis software is effectively used for site analysis of new stores, customer prospecting and other issues of decision making for business purposes, The same geoprocessing module of business analysis software would be useful if put to use for the management of disaster management especially for storm surge management. Application of the business analysis model for disaster management has been reviewed. Specially in case of storm surge, where quick response is crucial, the spatial decision support system will be most effective.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.52-57
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• 2011

Bridges are essential and valuable elements in road and rail transportation networks. Bridge remediation is a top priority for asset managers, but identifying the nature of true defect deterioration and associated remediation treatments remains a complex task. Nowadays Decision Support Systems (DSS) are used extensively to assist in decision-making across a wide spectrum of unstructured decision environments. In this paper a requirements-driven framework is used to develop a risk based decision support model which has the ability to quantify the bridge condition and find the best remediation treatments using Multi Attribute Utility Theory (MAUT), with the aim of maintaining a bridge within acceptable limits of safety, serviceability and sustainability.

• Journal of the Korea Institute of Building Construction
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• v.11 no.3
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• pp.238-246
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• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.145-154
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• 2001

This paper deals with a network model for the efficient transportation of post and consists of the formulation based on the network model and the LINGO programming model including the operations of the post transportation. This network model is represented by using Time Space Network. The generalized formulation is built up with the input variables and the decision variables, which are defined on the basis of the network model. And LINGO programming model to be proposed with DB and LINGO is constructed in consideration of how to manage the post transportation and the intermodal transport. The results of the model implementation were represented on Time Space Network and they are analyzed and verified. The LINGO programming model is used as the module to be set in application software. Specifically with using GEOmania, GIS tool, the LINGO Model is applied to develop the application for Mail Transportation Decision Support System.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.101-104
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• 1997

Providing information on corporate level decision making for multiple decision makers in a consistent way is essential in Decision Support Systems. However, since the decision makers have different background and knowledge, the models used by them are also different in representation models. This makes the decision makers require a lot of efforts for model integration in an integrated decision making. The purpose of this paper is to propose an integration mechanism for synthetic use of multi-abstraction level decision making models. The proposed integration mechanism consists of model interpretation phase, model transformation phase and model integration phase. Specifically, the model transformation phase is divided into model tightening mode which gather information to makes a model transformed into upper level model, and model relaxing mode which makes lower level model.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.133-149
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• 2014

It is unavoidable to provide products that meet customers' needs and wants so that firms may survive under the competition in this globalized market. This paper focuses on how to provide levels for attributes that compse product so that firms may give the best products to customers. In particular, its main issue is how to determine common attributes and the others with their appropriate levels to maximize firms' profits, and how to construct a decision support system to ease decision makers' decisons about optimal common attribute selection using the Semantic Web and SWCL technologies. Parameter data in problems and the relationships in the data are expressed in an ontology data model and a set of constraints by using the Semantic Web and SWCL technologies. They generate a quantitative decision making model through the automatic process in the proposed system, which is fed into the solver using the Logic-based Benders Decomposition method to obtain an optimal solution. The system finally provides the generated solution to the decision makers. This presentation suggests the opportunity of the integration of the proposed system with the broader structured data network and other decision making tools because of the easy data shareness, the standardized data structure and the ease of machine processing in the Semantic Web technology.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3045-3051
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• 1997

Generally, multi-criteria decisions are made by group of people because of their complexity. In the existing fuzzy aggregation method, the operators using minimum, maximum and average are used to aggregate the viewpoints of many staffs. These methods have problems in that they do not reflect the decision situation in the decision process. In order to solve these problems we propose a new fuzzy multi-criteria decision support systems model that aids the decision maker to aggregate the viewpoints of many staffs according to the decision situation. Moreover, we design the algorithms which can be used in the fuzzy multi-criteria decision support systems and develop its prototying system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.340-350
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• 2021

This paper proposes a decision support model for USVs to improve the accuracy of collision avoidance decision-making. It is formed by Navigation Safety Domain (NSD) and domain-based Collision Risk Index (CRI), capable of determining the collision stage and risk between multiple ships. The NSD is composed of a warning domain and a forbidden domain, which is constructed under the constraints of COLREGs (International Regulations for Preventing Collisions at Sea). The proposed domain based CRI takes the radius of NSD in various encounter situations as threshold parameters. It is found that the value of collision risk in any directions can be calculated, including actual value and risk threshold. A catamaran USV and 6 given vessels are taken as study objects to validate the proposed model. It is found that the judgment of collision stage is accurate and the azimuth range of risk exists can be detected, hence the ships can take direct and effective collision avoidance measures. According to the relation between the actual value of CRI and risk threshold, the decision support rules are summarized, and the specific terms of COLREGs to be followed in each encounter situation are given.

• Asia pacific journal of information systems
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• v.13 no.2
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• pp.105-117
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• 2003

As E-commerce is getting more popular, consumers faces more decision complexity than ever before because they can get greater alternative choices. Futhermore, consumers are sometimes motivated to expand effort in making a good decision in the case of relatively important and non-routine decison, such as buying a house or car. Thus, a buying decision support function is required to help consumers make decisions that have the greatest effects on consumer's lives. We develop a propotype system of a web-based buying decision support system for a cyber shopping mall. A buying decision of a consumer is a multi-attribute decision problem and is supported using analytic hierarchy process model. The system was implemented using HTML, Java, Java applet, JSP.