• Journal of Korean Institute of Industrial Engineers
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• v.39 no.3
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• pp.183-191
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• 2013

We address the power generation mix problem that considers not only nuclear and fossil fuels such as oil, coal and LNG but also renewable energy technologies. Unlike nuclear or other generation technologies, the expansion plan of renewable energy is highly uncertain because of its dependency on the government policy and uncertainty associated with technology improvements. To address this issue, we conduct a delphi survey and forecast the capacity of renewable energy. We further propose a stochastic mixed integer programming model that determines an optimal capacity expansion and the amount of power generation using each generation technology. Using the proposed model, we test eight generation mix scenarios and particularly evaluate how much the expansion of renewable energy contributes to the total costs for power generation in Korea. The evaluation results show that the use of renewable energy incurs additional costs.

• Asia Marketing Journal
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• v.6 no.2
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• pp.55-68
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• 2004

After Nakanishi and Cooper(1982) suggested a way of transforming the complicated nonlinear MCI model into a simple linear form, the application of MCI model has been increased. However, the use of MCI model in Korea is quite limited. The goal of this paper is to demonstrate the practical application of MCI(Multiplicative Competitive Interaction) model to a consumer goods industry. MCI model is a form of the attraction model explaining the relation between marketing mix variables and market share. In this study, multiple sources of empirical data are incorporated in the model formulation stage. In the estimation process, the equity estimation is applied to solve the possible multi-collinearity problem among marketing mix variables. Results from the fitted model suggest meaningful managerial implications for the management of brand equity and the allocation of resources among marketing mix variables.

• Steel and Composite Structures
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• v.29 no.1
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• pp.67-75
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• 2018

High performance concrete (HPC) depends on various parameters such as the type of cement, aggregate and water reducer amount. Generally, the ready concrete company in various regions according to the requirements and costs, mix design of concrete as well as type of cement, aggregates, and, amount of other components will vary as a result of moment decisions or dynamic optimization, though the ideal conditions will be more applicable for the design of mix proportion of concrete. This study aimed to apply dynamic optimization for mix design of HPC; consequently, the objective function, decision variables, input and output variables and constraints are defined and also the proposed dynamic optimization model is validated by experimental results. Results indicate that dynamic optimization objective function can be defined in such a way that the compressive strength or performance of all constraints is simultaneously examined, so changing any of the variables at each step of the process input and output data changes the dynamic of the process which makes concrete mix design formidable.

• The Korean Journal of Community Living Science
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• v.27 no.2
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• pp.281-294
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• 2016

This study was conducted to promote consumer interest in Geothermal Heat Pump (Ground Source Heat Pump, GSHP) and district heating and cooling (District Heating & Cooling, DHC) systems, which are competing with each other in the heating and cooling field. Considering not only the required cost data of energy itself, but also external influence factors, the optimal mix ratio of these two energy systems was studied as follows. The quantitative data of the two energy systems was entered into a database and the non-quantitative factors of external influence were applied in the form of coefficients. Considering both of these factors, the optimal mix ratio of GSHP and DHC systems and minimum Life Cycle Cost (LCC) were obtained using an algorithm model design. The Optimal Energy Mix of GSHP & DHC (OEMGD) algorithm was developed using a software program (Octave 4.0). The numerical result was able to reflect the variety of external influence factors through the OEMGD algorithm. The OEMGD model found that the DHC system is more economical than the GSHP system and was able to represent the optimal energy mix ratio and LCC of mixed energy systems according to changes in the external influences. The OEMGD algorithm could be of help to improve the consumers' experience and rationalize their energy usage.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.94-95
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• 2020

This paper introduces the mix design and performance evaluation of Ultra-High Performance Concrete (UHPC). The concrete mixture is designed to achieve a densely compacted cementitious matrix via the modified Andreasen & Andersen particle packing model. The compressive strengths of UHPC designed by this method reached 154MPa. The relationship between packing theory and compressive strength of UHPC is discussed in this paper.

• Korean System Dynamics Review
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• v.4 no.1
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• pp.33-53
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• 2003

How to maintain the optimal electric power generation mix is one of the important problems in electric power industry. The objective of this study is to develop a computer model which can be used to forecast the investment in power generation unit by the plant owners after restructuring of electric power industry. Restructuring of electric power industry will make difference in decision making process of investment in power generation unit. After Privatiazation of Power Industry, Gencos will think that profit is the most important factor among all others attracting the investment in the industry. Coal power generation is better than LNG CCGT in terms of profit. However, many studies show that LNG CCGT will be main electric power generation source because the rest of factors other than profit in LNG CCGT are superior than Coal power generation. Because the nst of factors other than profit in LNG CCGT are superior than Coal power generation. The impacts of the various government policies can be analyzed using the computer model, thus the government can formulate effective policies for achieving the desired electric power generation mix.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.2
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• pp.19-30
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• 1994

Generally, a planning problem in a flexible manufacturing system is considered to be a composite of three interdependent tasks : part selection, loading, and routing mix. This research presents a mathematical model which can concurrently solve part selection, loading, and routing mix problems, so the problems that are caused by treating the planning problems independently are solved. The mathematical model is aimed to minimize system unbalance and the number of late parts, including constraints such as machine capacity, tool magazine capacity, and tool inventory. To illustrate the application of the model, an example is included. Solution procedure based on Lagrangian relaxation is also suggested for larger-sized problems.

• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.87-102
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• 2021

The purpose of this study is to investigate customer satisfaction factors that affect customer loyalty and revisit intention, and the seven factors which comprise the marketing mix that affects customer satisfaction. loyalty, and intention to revisit. The purpose of the project is to propose a research model by testing the mediated effects of customer satisfaction and loyalty using mainly factor analysis, regression analysis, and mediation analysis. First the results showed that the marketing mix 7P factors influence customer satisfaction were identified as service delivery process, product, physical basis, and promotion. The factors that influence marketing mix 7P customer loyalty were tested in the order of service delivery, physical basis, product, and distribution. Second, the factors that affect customer loyalty were artists, service, and prices whereas the factors that affect customer satisfaction were tested in the order of service, artist, cosmetics, and price. Third, the factors affecting customer revisit intention were newly derived as treatment satisfaction, professionalism, and treatment products. Fourth, the relationship between marketing mix and customer revisit intention suggested that customer satisfaction and customer loyalty has a partial sale effect. It can be suggested on the basis of these findings that the effect of makeup service with marketing mix on customer revisit intention was analyzed and a new model was derived by analyzing the mediated effect of customer satisfaction and customer loyalty.

• Journal of Distribution Science
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• v.20 no.4
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• pp.33-50
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• 2022

Purpose: This pilot study aims to conceptualize a new green marketing mix for retail consumers based on Stimulus-Organism-Response (SOR) model. Moreover, it also aims to conceptualize a testable research model of new green marketing mix with consumers' green purchasing behavior, and to validate the measurement model with traditional as well as modern suggested validating techniques. Research design, data and methodology: A pilot test data from 75 respondents of retail buyers of energy-efficient electric appliances in Pakistan were tested for the confirmatory factor analysis (CFA) by examining a measurement model of the construct through different validation techniques (like Composite Reliability, McDonald's Omega (ω), rho (ρA), HTMT, etc.) as heretofore these scales were not validated through these modern methods. Results: The results revealed that the instrument has a certain degree of reliability and validity through different validating techniques. All the measurement items reach the suggested threshold values. Conclusions: Therefore, this study conceptualized an integrated framework of all the three stakeholders of the environment (government, companies, and public or consumers) to achieve environmental sustainability. Hence, future studies can extend these findings and conduct a full-scale study to establish an empirical relationship between the 5G's of green marketing for retailing businesses and consumers' green purchase behavior.