• Journal of Navigation and Port Research
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• v.46 no.2
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• pp.110-120
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• 2022

Spain is Europe's second-largest country with total throughput reaching 16.7 million twenty-foot equivalent units (TEU) by 2020. The purpose of this study was to measure and compare the efficiency of 17 container terminals. As a study method, the DEA-CCR model, undesirable variable, and Malmquist Index (MI) were used for data envelopment analysis (DEA). The study results are as follow: (1) DEA-CCR is used to evaluate basic efficiency. The most efficient terminals are decision-making units DMU 1 (APM Terminals (Algeciras Port)), DMU 2 (Total Terminal International Algeciras (Algeciras Port)) and DMU 5 (Barcelona Europe South Terminal (Barcelona Port)). (2) Undesirable DEA was conducted to suggest inefficiency from the undesirable output. Overall, the efficiency scores were reduced. However, DMU 1, DMU 2, and DMU 5 maintained efficiency scores regardless of the finish factor. (3) Malmquist Index was used to observe technology and efficiency changes dynamically. The changes in TCI affected Spanish container terminals more than the Technical Efficiency Change Index (TECI) in 2018-2019. However, in 2019-2020, the TECI was 2.706, higher than the TCI value, indicating that the change in TECI had more influence on the increase in productivity. This study offers a broader understanding of Spanish container terminals.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.123-135
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• 2016

This study uses Data Envelopment Analysis(DEA) to investigate the management efficiency of Korean shipping companies based on business administration costs such as corporate entertainment, advertisement, and labor costs. We analyze shipping enterprises listed on the Korean stock market of the period of 2010-2014. Corporate entertainment, advertisement and labor costs are used as input variables and sales and net income are used as output variables. We use technical efficiency, pure technical efficiency, scale efficiency and returns to scale to propose a plan to improve the efficiency of inefficiency decision-making units (DMUs). The results of the efficiency analysis show that six of the DMUs in the technical efficiency of CCR model and eight of the DMUs in the pure technical efficiency of BCC model are in efficient state. In terms of return to scale, six of the DMUs(24% of all DMUs) show increasing returns to scale, while 13 DMUs(52% of all DMUs) showdecreasing returns to scale. Because multiple efficient state for DMUs exist in the technical efficiency analysis, we conduct a super efficiency analysis. The results show that the efficient state of the twomost efficient DMUs are 1.314 and 1.243, respectively. This implies that these DMUs could maintain their current levels of the efficiency if they increase the amount spent on advertisements, corporate entertainment and labor costs by 31.4% and 24.3%. respectively. We conclude this study by providing the efficiency states of each DMU and target for improving the inefficiencies in each case.

• Korean Journal of Agricultural Science
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• v.39 no.2
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• pp.279-289
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• 2012

This study was conducted to survey and diagnose operation status of the agricultural machinery rental service, analyse and compare operational efficiency among 82 city and county ATDEC (agricultural technology development and extension center) using the DEA (Data Envelopment Analysis) method, and recommend future direction, for improvement of the business. Input variables were invested budget and labor, and output variable was rental return. Percentages of return to investment on the rental service were calculated as 68.3% and 63.9% when analyzed with CCR (Charnes, Cooper and Rhodes) and BCC (Banker, Charnes and Cooper) models, respectively, indicating inefficiency of the service operation. Increase of rental charge would increase efficiency by 63.9~68.3% depending on models, and decrease of financial and labor investment would improve the efficiency by about 11.3%. Technical efficiency would be more important than scale efficiency, therefore adjustment of over-invested budget and labor needed to be made together with increase of rental charge to improve the operation. Among the ATDECs providing the rental service, 6 (7.3%), 43 (52.4%), and 33 (40.2%) were in state of CRS (constant return to scale), IRS (increasing return to scale), and DRS (decreasing return to scale), respectively. These indicated public aspects of the rental system, over-investment, lack of output component for input component, meaning that scale income would be increased by qualitative expand of rental charge. Efficiency analysis of the rental system by region showed that efficient ATDECs to be benchmarked by others were in the order of DMU-70, DMU-54, DMU-29, DMU-5, DMU-22, DMU-2, and DMU-61. More comprehensive and extensive survey and analyses would be necessary in the future.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• Proceedings of the Materials Research Society of Korea Conference
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• 1999.05a
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• pp.142-142
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• 1999

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.293-298
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• 2005

Air-Launching is an effective method that can launch the 'Nanosat' with low launching cost. In this study, system and subsystem design of the air launching rocket for nanosats which perform a simple mission, have been performed. Foe this purpose, the WBS of the Air-launching Rocket System, and the subsystem schematics have been defined first. Based on these results, detailed configuration and DMU have been developed.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.32-41
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• 2010

DEA(Data Envelopment Analysis) is the relative efficiency measure among homogeneous DMU(Decision- Making Units) which can be used to useful tool to improve performance through efficiency evaluation and benchmarking. However, the general case of DEA was considered as unrealistic since it consists a benchmarking regardless of DMU characteristic by input and output elements and the high efficiency gap in benchmarking for inefficient DMU. To solve this problem, stratification method for benchmarking was suggested, but simply presented benchmarking path in repeatedly applying level. In this paper, we suggest a new method that inefficient DMU can choice the optimal path to benchmark the most efficient DMU base on the similarity among the input elements. For this, we propose a route choice method that combined a stratification benchmarking algorithm and SOM (Self-Organizing Map). An implementation on real environment is also presented.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• Journal of Energy Engineering
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• v.16 no.3
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• pp.93-102
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• 2007

This paper presents a scenario evaluation model of the AHP (Analytic Hierarchy Process) to evaluate dismantling scenarios considering quantitative and qualitative considerations. And decommissioning information producing modules which can obtain a dismantling schedule, quantify radioactive waste, visualize a radioactive inventory, estimate a decommissioning cost, and estimate a worker's exposure was developed to assess qualitatively decommissioning information. The digital mock-up (DMU) system was developed to verify dismantling processes and find error of scenarios in virtual space. It combines and manages the decommissioning information producing modules, the decommissioning DB, and the dismantling evaluation module synthetically. By using AHP model and DMU system, the thermal column in KRR-1 was evaluated on plasma arc cutting scenario and nibbler cutting scenario using the developed decommissioning DMU system.

• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.277-299
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• 1999

Since Chrysler Motor Co. had experienced the digital development system in the beginning of 1990's, most of leading automobile companies are trying to apply a digital information system for their own business process reengineering based upon concurrent engineering system from product planning phase. This is called as virtual DMU(Digital Mock-Up) system instead of the traditional PMU(Physical Mock-Up) system. By using the virtual prototype, all of the design requirements and system specifications can be checked, changed and optimized more quickly and more efficiently. This paper consists of five chapters for the DMU information system. In the 1$^{st}$ chapter, the principle of digital design system is suggested by using four basic modules such as product design module, process design module, manufacturing system design module and central control module. The basic scheme of DMU is introduced with the benefits of application in the chapter 2. In the chapter 3, a digital design process of new car development is explained with the detailed DMU design and design review processes. In the chapter 4, the practical DMU manufacturing techniques and applications are introduced as CAD/CAM analyses, DPA(Digital Pre-Assembly)reviews for development, production, operation and maintenance phases, digital tolerance analyses and digital factory analyses for assembling line simulation, automated robot welding processes, production jig ＆ fixtures and painting process simulation. Finally, the activities of digital design support; CAS-styling, CAE-engineering and CAT-testing are summarized for design optimization in the chapter 5. As today's automobile manufactures and related business organizations are struggling to compete in the global marketplace, they are concentrating on efficient use of DMU information system to reduce the new car development cost, to have shorten the delivery schedule and to improve product design quality. To meet the demand of those automobile industries on digital information systems, the CALS(Computer aided Acquisition and Logistics Support) and EC(Electronic Commerce)initiative has been focused as a dominant philosophy in defense ＆ commercial industries, specially automobile industries.s.