• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.42-55
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• 2010

This paper primarily deals with a decision-making for determining the number of voyages in each ship size under a specific port structure in order to minimize the total transport cost consisting of transport cost at sea, queuing cost in port, and inventory cost in yard. As a result of computer simulation using queuing model characterized by inter-arrival time distribution, we were able to find out some combination of voyage numbers of 3 ship-size(50,000-ton, 100,000-ton, and 200,000-ton), where the total transport cost can be minimized under a specific port structure. The simulation model also allows us to figure out any trade-off relationship among sea transport cost, queuing cost in port, and inventory cost in yard. Put it differently, an attempt to reduce the sea transport cost by increasing the number of voyages of the largest ship size, the transport cost incurred in both port and yard is hypothesized to be increased and vice versa. Consequently, Port managers are required to adjust the number of annual number of voyages allocated in each ship size, put into the sea lines for importing raw materials, in order to optimize the transport costs incurred under the specific port system. We may consider a net present value(NPV) model for performing an economic feasibility analysis on port investment project. If a total discounted net benefit, including cost savings, exceeds the initial investment for an additional berth construction, then we accept the port investment project. Otherwise, we reject the proposed port investment plan.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.12
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• pp.859-866
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• 2010

This study examined the economics of a solar water heating system for an office building using life cycle cost (LCC) optimization simulations. The numerical simulations were conducted with TRNSYS and GenOpt employing the Hooke-Jeeves algorithm. The solar collector area, slope, mass flow rate per collector area and storage tank volume were selected as the main design parameters of the solar water heating system. The LCC optimization simulations of the system were carried out for cases where water temperature was $60^{\circ}C$ and $50^{\circ}C$. The results showed that for water temperature at $60^{\circ}C$ and $50^{\circ}C$ the collector area could be decreased by 17% and 28%, storage tank volume could be decreased by 49% and 54%, and mass flow rate per collector area increased by 5% and 9% respectively compared to a non-optimized system. The LCC of the system was reduced by 4% for $60^{\circ}C$ and 7% for $50^{\circ}C$. The initial installation cost of the system was reduced by 24% for $60^{\circ}C$ and 34% for $50^{\circ}C$. However, the operating cost of the system increased by 16% for $60^{\circ}C$ and 36% for $50^{\circ}C$ compared to a traditional solar water heating system.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.1
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• pp.79-88
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• 2017

For the PBL contract, it is necessary for the contracting parties to share information regarding the reasonable inventory-level and the cost of its repair parts for the estimated demand. There are various models which can be used for this purpose. Among them, V-METRIC model is considered to be the most efficient and is most frequently applied. However, this model is usually used for optimizing the inventory level of the repair parts of the system under operation. The model uses a time series forecast model to determine the demand rate, which is a mandatory input factor for the model, based on past field data. However, since the system at the deployment stage has no operational performance record, it is necessary to find another alternative to be used as the demand rate of the model application. This research applies the V-METRIC model to find the optimal inventory level and cost estimation for repairable items to meet the target operational availability, which is a key performance indicator, at the time of the PBL contract for the deployment system. This study uses the calculated value based on the allocated MTBF to the system as the demand rate, which is used as input data for the model. Also, we would like to examine changes in inventory level and cost according to the changes in target operational availability and MTBF allocation.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.18-24
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• 1998

This paper attempts to improve domestic natural gas pricing system, thereby optimizing social welfare. This is done by deriving theoretical frameworks of natural gas pricing, which make use of both Ramsey component pricing rule and Efficient component pricing rule based on the theory of marginal cost. Allocative efficiency and social welfare between gas prices derived from the three pricing mechanism, present Cost-based pricing, Ramsey component pricing rule and Efficient component pricing rule, are analysed and compared in the case study. For the city gas, allocative efficiency of Cost-based pricing is higher than that of Ramsey component pricing rule and Efficient component pricing rule. In contrast, for the natural gas consumed for power generation, allocative efficiency of Cost-based pricing is lower than the other two pricing systems. It also turns out that social welfare is improved by the prices driven from Ramsey component pricing rule and Efficient component pricing rule rather than present Cost-based pricing.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.261-268
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• 2020

Machine learning is achieved by making a cost function from a given digital signal data and optimizing the cost function. The cost function here has local minimums in the cost function depending on the amount of digital signal data and the structure of the neural network. These local minimums make a problem that prevents learning. Among the many ways of solving these methods, our proposed method is to change the learning step-size. Unlike existed methods using the learning rate (step-size) as a fixed constant, the use of multivariate function as the cost function prevent unnecessary machine learning and find the best way to the minimum value. Numerical experiments show that the results of the proposed method improve about 3%(88.8%→91.5%) performance using the proposed method rather than the existed methods.

• Korean Journal of Agricultural Science
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• v.32 no.1
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• pp.81-94
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• 2005

This study is aimed at giving the basic information for individual farm households to make decisions for optimizing their farm sizes and for the government to implement farm size optimization policies through the identification of combinations among rice production factors in plain areas like Cheolwon district and the suggestion of the optimal farm sizes of individual farmers based on the scale of economy calculated. The data of agricultural production costs of 50 rice farmers in the plain area which is located in Dongsong-eup Cholwon district, Kangwon province were used in the analysis. The 'translog' cost function among various methods which is a flexible function type was adopted to calculate the scale of economy in rice production. Seemingly unrelated regression(SUR) method was used in forecasting functions and processing other statistics by SHAZAM which is one of the computer aid program for quantitative econometric analysis. In conclusion, the long-run average cost(LAC) curve showed 'U-shape' which was different from 'L-type' one which was shown in the previous studies by others. The lowest point of the LAC was 9.764ha and the concerned production cost amounted to 633 Won/kg. Based on these results, it have to be suggested that around 10 ha of paddy is the target size for policy assistances to save costs under the present level of farming practices and technology. The above results show that the rice production costs could be saved up to 10ha in Cheolwon plain area which is a typical paddy field. However, land use, land condition, land ownership and manager's ability which may affect scale of economy should be considered. Furthermore, reasonable management will have to be realized by means of labor saving technology and cost saving management skill like enlargement of farm size of rice.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.13-20
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• 2022

Aircraft weight is an important factor affecting performance and fuel efficiency. In the conceptual design stage of the aircraft, the process of balancing cost and weight is performed using empirical formulas such as fuel consumption cost per weight in estimating element weight. In addition, when an airline operates an aircraft, it promotes fuel efficiency improvement, fuel saving and carbon reduction through weight management activities. The relationship between changes in aircraft weight and changes in fuel consumption is called the cost of weight, and the cost of weight is used to evaluate the effect of adding or reducing weight to an aircraft on fuel consumption. In this study, the problems of the existing cost of weight calculation method are identified, and a new cost of weight calculation method is introduced to solve the problem. Using Breguet's Range Formula and actual flight data of the A350-900 aircraft, two weight costs are calculated based on take-off weight and landing weight. In conclusion, it was suggested that it is reasonable to use the cost of weight based on the take-off weight and the landing weight for other purposes. In particular, the cost of weight based on the landing weight can be used as an empirical formula for estimating element weight and optimizing cost and weight in the conceptual design stage of similar aircraft.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.814-816
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• 2002

Because there is a strong interaction between the arc discharge and their surroundings, it is not easy to predict the characteristics of industrial arc plasma systems such as gas circuit breakers. The design procedure of these systems is still largely based on trial and error, although the situation is rapidly improving because of the available computational power at a cost in which is still coming down. The desire to predict the behavior of arc plasma systems, thus optimizing and reducing the develpment cost, has been the motivation of these arc researches. In this paper. we have simulated the switching operation of a gas circuit breaker during high current area using a computational fluid dynamics considered the electric field analysis, the radiation model and the effeects of turbulence.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.