• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• Asia-Pacific Journal of Business
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• v.6 no.2
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• pp.1-17
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• 2015

This study compares the most widely used parametric and non-parametric techniques to measure cost and profit efficiency of banks, namely the Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We formulate the specification form of both stochastic cost and profit frontier models and constant return to scale Cost DEA and Profit DEA models and provide an empirical assessment of the cost and profit frontiers based on a panel dataset of National Commercial Banks (NCBs) and Private Banks (PBs) in Bangladesh over the 2001-2010 period. The cost inefficiency and profit efficiency are slightly higher for PBs than NCBs in case of both SFA and DEA. The coefficients of advance and off-balance sheet items are significant that positively influence the banks in stochastic cost frontier model while the advance, other earning assets, price of borrowed fund are significant and negative effects on the banks in stochastic profit frontier model. The average cost inefficiency and average profit efficiency are recorded with 16.3% and 91% respectively. The highest and lowest cost inefficiency are observed for Janata Bank and United Commercial Bank Limited whilst the highest and lowest profit efficiency are recorded for Eastern Bank Limited and Janata Bank respectively. The average technical and allocative efficiency are 68.8% and 35.9%, respectively in case of CRS cost-DEA model whereas they are 70.3% and 31.8% in case of CRS profit-DEA model. The average cost inefficiency is recorded 6.3% by SFA whereas it is 24.5% by DEA. The average profit efficiency is found 91% by SFA while it is 22.1% by DEA, and SFA method shows better bank efficiency than DEA.

• Journal of Korean Institute of Industrial Engineers
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• v.3 no.2
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• pp.63-72
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• 1977

The trend of computerization is significant in Korea even at its beginning stage, especially for governmental administration. However, full-fledged success of computerization in an organization is reported to be rare while the cost of computerization has been high and increasing. This paper is concerned with two features for the successful implementation of a computerized system in an organization selection criteria for the computerization among the possible candidate projects and project selection models. Due to the dearth of literature regarding successful implementation of computerization, other sources of literature with respect to R & D management, method engineering, etc. were reviewed to develop a set of factors influencing successful computerization. Thus, project selection criteria for computerization of governmental administration are developed and organized as follows: cost of computerization project including both system development and operating cost, quanitative and qualitative benefits of computerization project, probability of technical and implementation success of computerization and other organizational and political factors to be considered. These criteria are broken down into detailed sets of subcriteria to be measured. To select a project after thorough consideration of the selection criteria, a project selection model which takes into account all criteria together has to be developed. In the study three project selection models are suggested and developed. They are the checklist model, multi-stage cut-off model, and composite criteria model. A detailed procedure for each of the three models is illustrated. Although the project selection criteria and models are developed here primarily for the computerization of governmental administration, they are easily applicable to other settings of computerization. Finally, some caveats for the use of selection criteria and models are discussed.

• Steel and Composite Structures
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• v.47 no.2
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• pp.167-184
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• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.707-716
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• 2009

The role of cost modeller is to facilitate the design process by systematic application of cost factors so as to maintain a sensible and economic relationship between cost, quantity, utility and appearance which thus helps in achieving the client's requirements within an agreed budget. There are a number of research on cost estimates in the early design stage based on the improvement of accuracy or impact factors. It is common knowledge that cost estimates are undertaken progressively throughout the design stage and make use of the information that is available at each phase, through the related research up to now. In addition, Cost estimates in the early design stage shall analyze the information under the various kinds of precondition before reaching the more developed design because a design can be modified and changed in all process depending on clients' requirements. Parametric cost estimating models have been adopted to support decision making in a changeable environment, in the early design stage. These models are using a similar instance or a pattern of historical case to be constituted in project information, geographic design features, relevant data to quantity or cost, etc. OLAP technique analyzes a subject data by multi-dimensional points of view; it supports query, analysis, comparison of required information by diverse queries. OLAP's data structure matches well with multiview-analysis framework. Accordingly, this study implements multi-dimensional information system for case based quantity data related to design information that is utilizing OLAP's technology, and then analyzes impact factors of quantity by the design criteria or parameter of the same meaning. On the basis of given factors examined above, this study will generate the rules on quantity measure and produce resemblance class using clustering of data mining. These sorts of knowledge-base consist of a set of classified data as group patterns, of which will be appropriate stand on the parametric cost estimating method.

• Health Policy and Management
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• v.27 no.2
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• pp.149-156
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• 2017

Background: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. Methods: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures: model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication-days plus model 3). We evaluated model performance using $R^2$ at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. Results: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. $R^2$ values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding $R^2$ values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. Conclusion: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.

• IE interfaces
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• v.25 no.3
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• pp.347-356
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• 2012

The acquisition cost of defense weapon system has been continuously increasing because of art-of-technology of it. This phenomenon requires efficiency and transparency in the weapon system acquisition process through cost estimation. Therefore cost estimation is very important to the government acquisition programs to support decisions about funding and to evaluate resource requirement as a key decision point. The Commercial parametric cost estimating models have been using extensively to obtain appropriate cost estimates in early acquisition phase. These models have many restrictions to ensure the cost estimating result in Korean defense environment because they are developed based on foreign R&D data. Also estimation results are different from Korean defense industry accounting system. So, some studies have been tried to develop a CER (Cost Estimation Relationship) based on the Korean historical data. However, there are some restrictions to improve the predictability and ensure the stability of the developed singular CERs which consider the following data characteristics individually. The the abnormal conditions of data that is multicollinearity, outlier and heteroscedasticity under rack of the number of observations. In this paper, a CER's Linear Combining Model is proposed to overcome those limitations which guarantee more accurate estimation (25.42% higher precision) than other singular CERs. At least, this study is meaningful as a first attempt to improve the predictability of CER with insufficient data. The methodology suggested in this study will be useful to develop a complex Korean version cost estimating model development in future.

• Journal of Korean Society for Quality Management
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• v.43 no.4
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• pp.533-544
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• 2015

Purpose: The purpose of this study is to analyze the cost efficiency of food industry in JeollaNamdo. And this study is focused on the correlation between the economic efficiency of food industry and its cost efficiency, based on the analysis of 372 food companies' data in JeollaNamdo in 2012. Methods: DEA cost minimization is the measurement of the cost efficiency of JeollaNamdo food industry in 2012. In this study, the CCR and BBC models have been employed to analyze the decomposing cost efficiency-technical efficiency, allocative efficiency, and scale efficiency respectively. And the Spearman rank correlation and Wilcoxon signed rank test also have been employed to check the correlation and difference between the ranking orders based on the efficiency scores respectively. Results: For the CCR model, mean cost efficiency was found to be 0.084(0.54 for allocative efficiency and 0.19 for technical efficiency). For the BCC model, mean cost efficiency was found to be 0.252(0.453 for allocative efficiency and 0.564 for technical efficiency). Average scale efficiency was found to be 0.38. In analyzing the results, this study argues that the optimal way to improve cost efficiency is by reducing inputs proportionally and changing their combination. Conclusion: The efficiency scores of the two models show high correlation, whereas, the differences between them are also found to be significant. Hence, it should be cautious to select a suitable model when we do the research.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.204-214
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• 2010

A manufacturing/remanufacturing system is investigated with the consideration of required minimum quality of end-of-used products. A constant demand is satisfied by remanufacturing end-of-used products and manufacturing raw materials outsourced from outside. It is assumed in this system that the buyback price and remanufacturing cost are related to the different quality level of end-of-used products. For remanufacturing, only the used products that satisfy a required minimum quality level will be recycled. Thus, the returning rate is a function of the required minimum quality level. Functions of returning rate, buyback price and remanufacturing cost, which are closely connected to the quality level of end-of-used products, are investigated here. Treating the required minimum quality level of end-of-used products, the length of a cycle, the number of manufacturing lots and remanufacturing lots in a cycle as decision variables, the mathematical models with the objective of minimizing the average total cost are constructed. Through construction of a solution process based on Tabu Search algorithm and calculating examples, the validity of the models is illustrated.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.18-29
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• 2000

This study presents a cost of efforts estimation model under the environment of developing a software using component software package tools. The approach taken was to drive from variety of sources in an attempt to identify the most significant factors. These sources ranged from already existing cost models like COTS integration cost and COCOMO models to information gathered in a data collection survey. Once the candidate drivers had been identified, the next step was to interview with the experts who had been experienced more than 5 years in component development area to identify the most significant driving factors. From those selected drivers, I established the Cost Estimation Model which is suitable for the developing a software using component software package tools by applying the general from of the well-know COCOMO software cost estimation model. To established the best fit in Korean Software industry, I used Regression statistical analysis with 31 data collections.