• Smart Structures and Systems
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• 제26권4호
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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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.268-276
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• 2015

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold cross-validation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

• 한국IT서비스학회지
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• 제20권4호
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• pp.35-52
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• 2021

Nowadays data-based decision making is emerging as the center of the business environment paradigm, but many companies do not have data-driven decision-making systems. It has also been studied that using an expert's intuition in decision making can be more efficient in terms of speed and cost, compared to analytical decision making. The goal of this study is to analyze customer churn factors using a group of experts within a financial company from the viewpoint of decision-making efficiency. We applied a debit card 'A', product of the National Credit Union Federation of Korea. The churn factors of all the financial expert groups were examined. Also. the difference in each group (management support, card recruitment, customer service group) was analyzed. We expect that this study will be helpful in the practical aspects of managers whose environments is lack data-oriented infrastructure and culture.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.187-194
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• 1999

Case-Based Reasoning(CBR) systems support ill structured decision-making. The measure of the success of a CBR system depends on its ability to retrieve the most relevant previous cases in support of the solution of a new case. One of the methodologies widely used in existing CBR systems to retrieve previous cases is that of the Nearest Neighbor(NN) matching function. The NN matching function is based on assumptions of the independence of attributes in previous case and the availability of rules and procedures for matching.(omitted)

• 한국국방경영분석학회지
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• 제22권2호
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• pp.18-36
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• 1996

This paper provides a research on some design techniques of Industrial Engineering / Management Science (IE/MS) for more efficient logistics support in developing armament systems. It includes RAM (reliability, availability and maintainability) analysis techniques, decision making analysis techniques, human factors engineering techniques and logistics support analysis techniques.

• 대한산업공학회지
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• 제39권1호
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• pp.1-9
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• 2013

The current manufacturers have generated tremendous amount of digitized product data to efficiently share and exchange it with other stakeholders or various software systems for product development. The digitized product data is a valuable asset for manufacturers, and has a potential to support high level strategic decision makings needed at many stages in product development. However, the lack of studies on extraction of key performance indicators(KPIs) from product data management(PDM) databases has prohibited manufacturers to use the product data to support the decision makings. Therefore this paper examines a possibility of an architecture that supports KPIs for evaluation of product development performances, by applying multidimensional product data model and on-line analytic processing(OLAP) to operational databases of product data management. To validate the architecture, the paper provides a prototype product data management system and OLAP applications that implement the multidimensional product data model and analytic processing.

• Asian-Australasian Journal of Animal Sciences
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• 제12권3호
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• pp.435-444
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• 1999

This paper summarizes the size and output of the major animal industries in Australia and the feed resource available to maintain production. The most important feed source is pasture but there is also extensive use of cereal grains, pulses and by-products in the intensive animal industries and in supplementing the diet of grazing animals. These resources must be used in ways that ensure sustainable production. We outline a number of Decision Support Systems such as GrazFeed, GrassGro, and AusPig which play an important role in optimizing the way in which resources are used. Waste management with respect to mineral pollution of water courses and methane production as a greenhouse gas are important issues for the animal industries and are also considered.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.252-269
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• 1995

Organizational decision support systems(ODSS) are a new type of decision support systems(DSS) focusing on the organization-wide issues rather than individual, group, or departmental issues. Because of its organization-wideness, which means an ODSS cuts across organizational functions or hierarchical layers, seamless integration with organization's diverse IS applications running on heterogeneous platforms becomes a critical issue. In this paper, we analyzed the Korea Telecom(KT)'s Operations & Maintenance(O&M) division focusing on its investment strategies. We developed a conceptual framework which links O&M investment to its performance. We also developed a prototype of KTOM-ODSS with an EIS-like user-friendly interface.

• Asia pacific journal of information systems
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• 제13권2호
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• pp.23-46
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• 2003

This paper proposes an analysis methodology that enables the establishment of improved customer satisfaction via decision support system using customer satisfaction index data and customer database of a company. The proposed methodology establishes rational future goal of a company by applying DEA, finds potential customers which correspond to demographic features of the previous target group, and improve quality factors which distinguish the quality-satisfaction-group from the quality-dissatisfaction-group through the use of machine learning tools, SOM and C4.5. Finally, we illustrate the effectiveness of our research methodology using actual data of a camera company.