• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.351-356
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• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.228-238
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• 2009

This paper first presents two supply chain design problems: 1) a factory location setting and factory selection problem, and 2) a factory location setting and factory/supplier selection problem. The first involves a number of location known retailers choosing one factory to supply their demands from a number of factories whose locations are to be determined. The goal is to minimize the transportation and manufacturing cost to satisfy the demands. The problem is then augmented into the second problem, where the procurement cost of the raw materials from a chosen material supplier (from a number of suppliers) is considered for each factory. Economic beneficial is taken into account in the cost evaluation. Therefore, the partner selections will influence the cost of the supply chain significantly. To solve these problems, an agent gaming and genetic algorithm hybrid method (AGGAHM) is proposed. The AGGAHM consecutively and alternatively enable and disable the advancement of agent gaming and the evolution of genetic computation. Computation results on solving a number of examples by the AGGAHM were compared with those from methods of a general genetic algorithm and a mutual frozen genetic algorithm. Results showed that the AGGAHM outperforms the methods solely using genetic algorithms. In addition, various parameter settings are tested and discussed to facilitate the supply chain designs.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.87-94
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• 2023

This study analyzed and identified various causes of caustic alarms of 163 fire detectors that occurred from January 2019 to December 2021 at domestic semiconductor manufacturing plants equipped with about 30,000 fire detectors, and proposed a new non-fire prevention cause investigation plan by applying the NFPA 921 scientific methodology. The results of the study are as follows. First, in terms of necessary recognition and problem definition, an analog detector and an integrated monitoring system were proposed to quickly determine the location and installation space information of the fire detector. Second, in order to prevent speculative causes and errors in various analyses in terms of data analysis and hypothesis establishment, non-fire reports were classified into five by factor and defined, and the causes of occurrence by factor were classified and proposed. Finally, in terms of hypothesis verification and final hypothesis selection, a non-fire prevention improvement termination process and a final hypothesis verification sheet were proposed to prevent the cause from causing re-error.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.135-146
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• 2014

The improvement of adaptation capability against heat island (ACHI) by greening buildings is considered as an important measure to cope with a climate change. This study aimed to select the most appropriate zones for green roof initiative in case study sites, Bucheon, Anyang, and Suwon Cities and to investigate the characteristics of buildings for greening to improve ACHI. Relative ACHI for each lot was estimated from 0 to -9, assuming that it decreases with the distance from green space and waterbody. Low adaptation capabilities were mostly shown in the old urban blocks with dense low-rise buildings and lack of green space. Three blocks with the lowest ACHIs were chosen as a green roof initiative zone in each city. They are largely residential areas including low-rise buildings such as single, multi-household houses, townhouses, 5 or lower story apartments and few are industrial areas crowded with small factory buildings. The areas of building roof available for greening are 8.8% within the selected zones in Bucheon City, 5.3% in Anyang City, and 4.9% in Suwon City. As it were, 25.2~41.7% of the roof top areas are available for greening in these zones. It means that roof top areas of $25,000{\sim}120,000m^2$ can be used for greening within the selected zones of $0.64{\sim}1.65km^2$ to improve ACHI. The approach and results of the study are significant to provide a logical basis and information on location, scale, effect, and target figure of greening as a measure to cope with climate change.