• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.679-680
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• 2011

This paper is developed to Energy Balance Flow show the flow of total energy resource be used nationally. The Energy Balance Flow is applicable of demand management factor through the analysis of foreign energy model of supply and demand and energy statistic data in the country. This study is based on and developed to Energy system management model is able to appraisal efficient of energy cost cutting, CO2 emission reduction and Energy saving at the national level calculated effect reached amount of primary energy to change of energy flow followed application of demand side management factor is able to appraisal quantitatively at the total energy to model of demand and supply.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.831-839
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• 2008

The steel industry is becoming more important around the world and the demand of steel is increasing. Korea is the 5th country of steel producing in the world and the attention in the steel industry is growing. The steel industry is one of the key industry in leading the economic growth in Korea. This study attempts to analyze by time-series the economic impacts of the steel industry using an inter-industry analysis Specifically, the study investigates production-inducing effect, value added inducing effect and employ-inducing effect of the steel industry based on demand-driven model and the study deals with supply shortage effect and sectoral price effect of the steel industry by using supply-driven model and Leontief price model.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• Korean Journal of Construction Engineering and Management
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• v.12 no.5
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• pp.35-45
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• 2011

Establishing housing supply strategies in Korean housing market is a crucial issue due to contradictory but concurrent two problems in market; one is the unstable working-class residential and the other is the high vacancy rate by the low-level of sales rate. Although government has been continuously implementing various supply policies in an attempt to evenly distribute houses as well as to keep supply and demand in balance, it is difficult to satisfy all of stakeholders, such as housing consumers, housing owners and housing suppliers. This paper, therefore, applies a system dynamics methodology and offers a dynamic and integrated model encompassing for-profit behaviors of each market participants. The proposed model simulates the future trends of house prices, the balances between supply and demand, construction companies earnings and vacancy rate when applying various housing supply scenarios. From the simulation result, recent governmental small-size rental housing supplies in bulks should utilize private construction companies to stabilize housing distribution rate and private supply system as well as the supply and demand are well balanced.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.334-337
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• 2003

In this paper we presents a centralized model for a long-term replenishment contract model in the supply chain system. We assume ARIMA demand process for reflecting more realistic demand data and present a solution which minimizes total system cost of the contract model between single supplier and buyer under centralized system. From the result of experiments we can observe that the proposed model generate better result than the decentralized model.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.29-41
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• 2020

Wild pollinators provide important pollination services for crops. However, their geographical ranges and impact on pollination services have not been fully explored within the scope of Korean agricultural land. This study aims to identify spatial supply-demand mismatches across orchard fields in the context of assessing pollination service risk. We first used National Ecosystem Survey data and a species distribution model (MaxEnt) to develop the geographic range of each of 32 wild pollinators belonging to three families (Diptera, Hymenoptera, and Lepidoptera). We then summed the modeled presence probability of each species to obtain a measure of spatially explicit pollinator richness. This modeled richness, defined as pollination supply, was compared with the summed area of orchard fields at the municipal boundary level to identify areas with supply-demand mismatches. The study found that Lepidoptera showed the highest species richness (8.3±1.5), followed by Hymenoptera (4.3±0.8) and Diptera (3.5±0.8) species. Median orchard area was 1.5 ㎢ (range of 0-176.7 ㎢) among 250 municipal regions in South Korea. The municipal regions were divided into three categories (tertiles) of low, middle, and high pollination supply and demand according to, respectivley, average polliator richness and orhard area. Finally, we found that 55 municipal regions (accounting for 49% of national orchard land) potentially faced high risk of pollination deficits, 81 regions (48% of national orchard land) faced intermediate risk, and 63 regions faced low risk (3% of national orchard land). In conclusion, this study revealed significant mismatch between pollination supply and demand and developed risk assessment map will guide our future efforts on pollinator habitat conservation and monitoring to conserve crop pollination services.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.61-76
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• 2009

We try to use system dynamics to forecast the demand/supply and price, also transportation fare for iron ore. Iron ore is very important mineral resource for industrial production. The structure for this system dynamics shows non-linear pattern and we anticipated the system dynamic method will catch this non-linear reality better than the regression analysis. Our model is calibrated and tested for the past 6 year monthly data (2003-2008) and used for next 6 year monthly data(2008-2013) forecasting. The test results show that our system dynamics approach fits the real data with higher accuracy than the regression one. And we have run the simulations for scenarios made by possible future changes in demand or supply and fare related variables. This simulations imply some meaningful price and fare change patterns.

• Korean Journal of Chemical Engineering
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• v.36 no.1
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• pp.12-20
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• 2019

An energy storage system consisting of a battery and a power-to-methanol (PtM) unit was investigated to develop an energy storage system for renewable energy systems. A nonlinear programming model was established to optimize the energy storage system. The optimal installation capacities of the battery and power-to-methanol units were determined to minimize the cost of the energy system. The cost from a renewable energy system was assessed for four configurations, with or without energy storage units, of the battery and the power-to-methanol unit. The proposed model was applied to the modified electricity supply and demand based on published data. The results show that value-adding units, such as PtM, need be included to build a stable renewable energy system. This work will significantly contribute to the advancement of electricity supply and demand management and to the establishment of a nationwide policy for renewable energy storage.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.147-154
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• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.97-109
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• 2014

This paper considers a reverse supply chain with simultaneous recovery of used products and manufacturing of brand-new ones. Recovered products are downgraded and have to be sold in a market different from that of brand-new products at a different price. In case of a shortage of recovered product inventory, a brand-new item, if available, can be offered at the price of a recovered product. In other words, one-way demand substitution is allowed. We address the joint decision of when to manufacture brand-new product, when to recover returned product, and how to control demand substitution to maximize the hybrid production system's profits. To this end, we propose a Markov decision Process model and investigate the structure of the optimal policy. Performance comparison is numerically implemented between the models with and without downward demand substitution option under different operating conditions of the system parameters.