• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.158-167
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• 2018

The role of S/W in weapon systems has been developed with various functions and complex structures. As the weapon system S/W is directly related to quick and accurate decision making of users, more accurate evaluation is required during the training. However, situation awareness of weapon system S/W users has only been assessed qualitatively such as by simple test or qualitative judgement. Therefore, this study suggests the 'Cognition Ratio' concept which represents the quantitative of users by combining ACT-R cognitive architecture to SA (Situation Awareness) and Fitts' Law based on the theory of cognitive engineering. The cognition ratio is a ratio of cognition among the whole cognitive behavior process including perception and psychomotor. Moreover, this study provides a systematic assessment process of situation awareness that will be applicable to various weapon system S/W for effective assessment.

• The Journal of Information Systems
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• v.19 no.4
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• pp.51-63
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• 2010

U-City applies ubiquitous information technologies such as RFID, GPS, USN to various components of city functions and services. The concept of U-City was popularized especially in Korea and currently more than 40 projects have been carrying out all over country. U-City incorporates advanced information communication technologies into ubiquitous information services to provide better quality of life. The purpose of this study is to analyze preferences for the U-City services by surveying experts in U-City developing companies. This study employs Analytic Hierarchy Process which is very useful tool for performing multi-criteria decision making. Total of 28 responses were used in the analysis. The results indicated that the first 7 most preferred items were from transportation and safety area and environment and healthcare area and 4 out of 6 items in transportation and safety area were ranked among them. It implies that respondents consider countering anxiety caused by congested traffic, natural disasters, crimes, etc most important aspect that U-City should deal with. On the other hand, U-Port, U-Convention, U-Logistics, U-Public Administration and U-City Portal were listed as the least preferred services.

• Journal of Navigation and Port Research
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• v.29 no.3 s.99
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• pp.195-201
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• 2005

This paper presents the measurement of efficiency for container ports. Data envelopment analysis(DEA), as it is called, has particular applicability in the service sector. Applying mathematical programming techniques, DEA enables relative efficiency ratings to be derived within a set of analysed units. This paper investigates the efficiency employing DEA Model on data for 53 container ports covering 1995-2001 in the world and the change in efficiency for 7 years. As a results, port of Busan was evaluated as inefficiency port compare with major ports of the world except 1995year and 1996year. But After 1997year, efficiency of Busan port is increasing somewhat better every year.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.462-468
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• 2019

Offshore oil production faces several difficulties caused by oil price decline and unexpected changes in the global petroleum logistics. This paper suggests a stochastic model for optimizing the offshore oil production under uncertainty. The proposed model incorporates robust optimization and restricted recourse framework, and uses the lower partial mean as the measure of variability of the recourse profit. Some computational experiments and results based on the proposed model using scenario-based data on the crude oil price and demand under uncertainty are examined and presented. This study would be meaningful in decision-making for the offshore oil production problem considering risks under uncertainty.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.298-312
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• 2013

This paper compares efficiency results and performance targets for academic departments in a local private college using DEA (Data Envelopment Analysis). Because of an aging society, a smaller school-age population entering colleges, and enhanced accreditation standards by the government, colleges and universities are not recruiting and retaining sufficient students and therefore are struggling for survival. In contrast to popular four-year undergraduate universities concentrated in Seoul and its satellite cities, retaining students is critical for the survival of local private colleges in poor or remote regions. Therefore, it is very important to identify the factors involved in the retention of students in the various departments of a college. However, given the different characteristics of the departments, it is difficult to identify one unique or robust set of standards to evaluate their performance. The purpose of this paper is to maximize student retention capabilities by ensuring that additional resources are assigned to efficient DMUs, while, inefficient DMUs are given benchmarked targets. Based on previous studies and college accreditation standards, this paper presents indices to be used in evaluating the efficiency of academic departments in a college. In evaluating relative efficiency, this paper uses the output-oriented BCC model. To define target levels to be achieved for efficient DMU, a multi-stage DEA procedure is used.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.163-170
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• 2006

This paper presents a approach to the measurement of efficiency. Data envelopment analysis(DEA), as it is called, has particular applicability in the service sector. Applying mathematical programming techniques, DEA enables relative efficiency ratings to be derived within a set of analysed units. This paper investigates the efficiency employing DAE-CCR Model and DEA-BCC Model on data for 15 container terminals covering 1998$^{\sim}$2005 in Korea Results of this paper, suggests to some plan for operation strategy in Container terminals.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.131-145
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• 2020

The numbers of SNS (Social Network Service) users and usage amounts are increasing every year. The influence of SNS is increasing also. SNS has a wide range of influences from daily decision-making to corporate management activities. Therefore, proper analysis of SNS can be a very meaningful work, and many studies are making a lot of effort to look into various activities and relationships in SNS. In this study, we analyze the SNS following relationships using Twitter, one of the representative SNS services. In other words, unlike the existing SNS analysis, our intention is to analyze the interests of the accounts by extracting and visualizing the accounts that two accounts follow in common. For this, a common following account was extracted using Microsoft Excel macros, and the relationship between the extracted accounts was defined using an adjacency matrix. In addition, to facilitate the analysis of the following relationships, a direction graph was used for visualization, and R programming was used for such visualization.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.331-336
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• 2004

In order to strengthen the competitiveness of port against calling for the huge vessel and reducing the shipping service time, the productivity of container terminal must be improved. This productivity variously results according to the kinds of productivity evaluation model, input elements like yard, equipment, employee, facility, etc,. But, it is discussed that the productivity is measured by partial productivity evaluation model or general input elements. Therefore, we measured for the productivity of the container terminal using the Developed the data Envelopment Analysis (DEA), which is developed in order to evaluate the relative efficiency of decision making units - it's difficult to clear cause and effect between input and output. We measured the whole productivity of container terminal in Busan according to decision of the correct input elements. And we investigated the change of the productivity measurement result according to input elements, presents more accurate productivity evaluation model in container terminal.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.