• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.198-208
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• 2011

At a shipyard, the efficient handling of blocks is one of the most important factors in the shipbuilding process. We consider the problem of deployment planning of blocks from storage yards. As some information of block arrangement should be considered to handle the problem, we adopt the block arrangement based on the coordinates and sizes of each block at a storage yard. Deployment planning for a block involves deciding upon its transportation route from the storage yard and searching for blocks that would obstruct its transportation along this route. A tabu search algorithm for deploying several blocks is developed to minimize the number of obstructive blocks deployed together from the storage yards at a shipyard. The results of computational experiments show that the developed algorithm is very useful in the deployment planning of multiple blocks from the storage yards.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.1-10
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• 2013

The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.687-693
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• 2012

In this paper, we propose a optimal deployment method for cooperative exploration of swarm robots. The proposed method consists of two parts such as optimal deployment and path planning. The optimal area deployment is proposed by the K-mean Algorithm and Voronoi tessellation. The path planning is proposed by the potential field method and A* Algorithm. Finally, the numerical experiments demonstrate the effectiveness and feasibility of the proposed method.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.279-290
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• 2014

Purpose: The purpose of this study is to propose adoption of QFD and Stage-gate in order to analyze the quality of korea defense system. Methods: Drawing change data of initial production phase in korea defense system were anlayzed and a practical method was proposed. Results: The results of this study are as follows; Off line Quality Control should be introduced in development phase. Specially, in case of defense system, the best method is QFD(Quality Function Deployment) and Stage-gate process. At first, QFD 1 step defines product planning from VOC(Voice Of Customer), QFD 2 step specifies part planning from product planning, QFD 3 step defines process planning from part planning, QFD 4 step defines production planning from previous process planning. Secondly, Stage-gate process is adopted. This study is proposed 5 stage-gate in case of korea defense development. Gate 1 is located after SFR(System Function Review), Gate 2 is located after PDR(Preliminary Design Review), Gate 3 is located after CDR(Critical Design Review), Gate 4 is located after TRR(Test Readiness Review) and Gate 5 is located before specification documentation submission. Conclusion: Off line QC(Quality Control) in development phase is necessary prior to on line QC(Quality Control) in p roduction phase. For the purpose of off line quality control, QFD(Quality Function Deployment) and Stage-gate process can be adopted.

• Journal of Korean Society for Quality Management
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• v.30 no.1
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• pp.61-73
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• 2002

The Quality Function Deployment(QFD) is a Quality Management technique to maximize customers' satisfaction by reflecting customer requirements into all business processes, including concept definition, product planning, parts planning, process planning, production planning, and sales planning. The basic concept of the QFD is to translate customers' requirements appropriately into engineering characteristics, into parts characteristics, into process characteristics, and into specific requirements and activities in production. In this study, we reviewed and analyzed the application process of the QFD to the development of A2 FATC (Full Automatic Temperature Controller), an automotive component developed and produced by company A. It has been reported that by applying the QFD to the development of A2 FATC, company A. achieved 34% improvement in control robustness quality characteristic, 27% improvement in deviation quality characteristic, and 30% improvement in overall quality characteristics.

• Korean Management Science Review
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• v.13 no.3
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• pp.91-104
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• 1996

The basic idea of Quality Function Deployment(QFD) is to deploy the voice of customers into the final product through product planning, part planning, process planning, and manufacturing. In the product planning stage, which is the first stage of product development, customer attributes(CAs) are translated into engineering characteristics(ECs). Then, based on the relationship between CAs and ECs, the target values of ECs are determined. In the previous research, the process of analyzing these relationships is mostly subjective in nature. In this article, we formulate the process of determining the target values of ECs as an optimization model. That is, we first determine the relationship between CAs and ECs as cumulative logit models and construct constraints into which the company strategy as well as the needs of customers can be incorprated. Next, cost functions of ECs are developed, which are summed into an objective function. An algorithm to solve the formulated optimization problem is developed and illustrated with an example.

• Journal of the Korean Society of Food Culture
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• v.33 no.2
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• pp.199-205
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• 2018

This study has utilized the Quality Function Deployment (QFD) technique in order to plan the tangible (menu) and intangible (service) product of dumpling restaurant. The engineering characteristics of tangible products were classified into product planning, purchase management, production management, and information management based on the production system of foodservice. The engineering characteristics of intangible products were also classified into physical evidence, human interaction, and pre-communication based on the service operation and delivery system. As a result of analyzing the QFD, it was found that the customer hope the hygiene factor and response factor to be improved. It is analyzed that product planning, information management, and production management should be improved first in terms of engineering characteristics considering consumer needs. In the future, by utilizing the systematic product development process that the requirements of tangible and intangible product consumers are converted to the engineering characteristics, the development of competitive product within the market will be possible, and furthermore it is expected to be useful for reducing the unnecessary time and design costs due to failure of product development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2173-2193
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• 2013

In order to reduce the distribution cost of sensor nodes, a mobile sensor deployment has been proposed. The mobile sensor deployment can be solved by finding the optimal layout and planning the movement of sensor nodes with minimum energy consumption. However, previous studies have not sufficiently addressed these issues with an efficient way. Therefore, we propose a new deployment approach satisfying these features, namely a tree-based approach. In the tree-based approach, we propose three matching schemes. These matching schemes match each sensor node to a vertex in a rake tree, which can be trivially transformed to the target layout. In our experiments, the tree-based approach successfully deploys the sensor nodes in the optimal layout and consumes less energy than previous works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1568-1589
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• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.462-483
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• 2016

The research on optimization of cellular network's energy efficiency (EE) towards environmental and economic sustainability has attracted increasing attention recently. In this survey, we discuss the opportunities, trends and challenges of this challenging topic. Two major contributions are presented namely 1) survey of proposed energy efficiency metrics; 2) survey of proposed energy efficient solutions. We provide a broad overview of the state of-the-art energy efficient methods covering base station (BS) hardware design, network planning and deployment, and network management and operation stages. In order to further understand how EE is assessed and improved through the heterogeneous network (HetNet), BS's energy-awareness and several typical HetNet deployment scenarios such as macrocell-microcell and macrocell-picocell are presented. The analysis of different HetNet deployment scenarios gives insights towards a successful deployment of energy efficient cellular networks.