• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.375-380
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• 2003

This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

• Proceedings of the IEEK Conference
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• 2004.06b
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• pp.481-484
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• 2004

To make production and equipment investment plans in semiconductor Line, implementation of many variables is needed. But these factors could bring many changes and the result is hard to predict. Because prediction is hard, it is hard to make a standard. So this project established Semiconductor production plans using LP Algorithm to satisfy all the conditions from the factors and came up with thesis on reasonable and standardized process.

• Journal of the military operations research society of Korea
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• v.36 no.2
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• pp.1-10
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• 2010

Learning rate is generally applied to estimate an appropriate production labor cost. Learning effect is obtained from repetitive work during the production period under 3 assumptions ; homogeneous production, same producer, quantity measure in continuous unit. However, production breaks occur frequently in Korean defense industry environment because of budget constraint and annual requirements. In this case previous learning effect can not be applied due to learning loss. This paper proposed the application of learning rate when a production break occurs in Korea defense industry. To obtain a learning loss, we surveyed various learning loss factors for different production breaks(6, 12, 18 months) from 4 defense industry companies. Then, we estimate the first unit labor hours in re-production phase after production break using Anderlohr method and Retrograde method with the result of the survey. This work is the first attempt to show a method which defines and evaluates the learning loss factors in Korean defense industry environment.

• Journal of the Korean Regional Science Association
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• v.9 no.2
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• pp.103-119
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• 1993

This research examines the spatial development of rural industrial estates (Nong-gong Jigu) in Chonbuk province and gives insight into the strategies for economic development in the entire region. Selected location factors which are likely to pull new investment into the estates are examined by using questionnaires. Few loction factors except nonlocal factors can be found in explaining why location choices are made. The irrelevance of the analysis based on location factors suggests that an alternative approach should analyze changes in the spatial development of the rural industrial estates. Such an alternative is to understand the dynamics of the spatial organization of production by focusing on characteistics of plant closing in the rural industrial estates. To take into account of the characteristics of plant closing we provide the hypothesized relationships between employment size, organizational structure, inter and intra industrial linkage, characteristics of production processes, and availability of local labor market and the likelihood of closing. A logit model is then made to identify the selected factors which might influence the probability of plant closing. The results from the logit analysis and their implications suggest that the policy should be more concerned with the characteristics of firms, such as size and ownership, as well as of the local labor markets. Given that the Chonbuk region has experienced rapid population decline, together with its poor industrial base, it seems that the success of the policy in the declined rural areas in less certain.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.135-139
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• 2008

Seed production and germination could be influenced by some factors. The picking time and climate factors are regarded as the elements to obtain sound seeds. We have observed the seed productivity and germination of seeds from trees of Chamaecyparis obtusa selected in a clonal seed orchard. Depending on picking time, various shapes from liquid material, something jellied to the fully matured one were observed. Germination aspects varied throughout the test days. After 20days of seeding in a glass petri-dish, germinal apparatuses appeared from the all seeds which had been picked from after at the end of August. The highest germination rate of about 30% was observed from the seeds picked from $20^{th}$ of September and $10^{th}$ of October. Seed production was about two times higher in 2005 than in 2006 and the average germination rate was also higher in 2005. We have also analyzed the effects of climatic factors about two consecutive years on seed productivity. Among the climatic factors, monthly sum of temperature and of precipitation were the main factors for maturation of Chamaecyparis obtusa seeds.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.167-179
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• 2022

The study aims to determine the influencing factors and their impact on the management of the forest land used for production allotted to peasant households and proposes solutions to improve its management. Secondary data was calculated until the end of 2019 at state agencies. The primary data was collected through 2 steps. To determine the factors influencing forest land management for production, step one involved a survey of 100 households that were given access to forest land. Step 2 involved a survey of 215 households to determine how factors impacted the Likert scale's five levels. The study also used a multivariate regression model and SPSS24.0 software to determine the impact rates of factor groups. The study indicated 43 factors that belong to 11-factor groups affecting the management of forest land for production. The policy and legal factors group is the most influential, with an impact rate of 12.72%, followed by 10 other factor groups with impact rates ranging from 4.08% to 11.74%. The solutions include improving policies and laws, strengthening the dissemination of policies and laws; encouraging investment, completing cadastral work, intensifying inspection, examination, and sanctioning of administrative violations of land, upgrading the infrastructure system, and ensuring enough human resources to manage forest land.

• Journal of Microbiology and Biotechnology
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• v.8 no.1
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• pp.81-88
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• 1998

The determination of appropriate parameters and parameter conditions is very important for the optimization of production of target materials. Taguchi's method has been used widely as the basis for development trials and optimization during industrial process design. Reaction variables which influence product yield are easily determined and their effects are revealed by just a few reactions, negating the need for extensive experimental investigation. There are usually some factors that are responsible for variations in process characteristics, so called noise factors. Controlling noise factors is very costly and difficult or impossible. Taguchi's experimental design method was examined to determine the control factor's level that is less sensitive to the changes in environmental conditions and other noise factors without control of noise factors. In this study, optimization of lipase-catalyzed production of lysophosphatidic acid (LPA) which has various physiological functions was performed by Taguchi's method. We obtained LPA yields ($66.5\%$) with low variance (5.32) at 400 RPM, molar ratio of 40 : 3 (mol) (fatty acid: G-3-P), 48 h, and $50^{\circ}C$. Thus, bioactive LPA with a desired fatty acid moiety could be produced with high yields and low variance despite various environmental noise factors.

• KSBB Journal
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• v.16 no.2
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• pp.207-211
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• 2001

Statistical experimental design methods were employed to select the cultivation factors influencing latic acid production during the fermentation of kitchen refuses. Working volume and pH swings were identified as the main factors affecting lactic acid production. Optimum pH swing was pH 7.8 and working volume was 125 mL in a 250 mL flask. Under optimum condition, lactic acid was produced at 21.8 g/L, which was 6.2 times higher than produced during uncontrolled fermentation.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.1-9
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• 2022

The study aims to determine the factors and their influence on the income from using public agricultural land of households. Public agricultural land is agricultural land, including land for growing annual crops, perennial crops, and land for aquaculture, leased by commune-level People's Committees with a lease term of not more than 5 years. Secondary data were collected for the 2017-2021 period at state agencies. Primary data were collected from a survey of 150 households renting public agricultural land. The regression model assumed that there were 28 factors belonging to 7 groups. The test results show that 25 factors affect income, and 03 factors do not. The group of COVID-19 pandemic factors has the strongest impact, followed by the groups of agricultural product market factors, land factors, capital factors, production cost factors, labor factors, and climatic factors. The impact rate of COVID-19 pandemic factors is the largest (23.00%); The impact rate of climatic factors is the smallest (6.04%). Proposals to increase income include good implementation of disease prevention and control; increasing the land lease term; accurately forecasting the supply and demand of the agricultural market; raising the level of the household head; ensuring sufficient production capital, and adapting to the climate.