• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.127-130
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• 2000

In this research, the system is assumed to carry a single item of which the demand types vary. Demand type is defined as a management's classification of the item according to the demand source or to the service purpose. The purpose of this research is to find the optimal inventory control policy when the system carries a single item which consists of multiple demand types. In this research, the optimizing algorithm contains a heuristic, therefore, the optimal Is not guaranteed by the algorithm. At least, this research provides the solution to the problems that have not been solved by the existing algorithms.

• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.151-157
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• 1993

In this paper, we investigate the effects of yield randomness for lot-sizing in a multi-stage production system. The practical importance of incorporating yield randomness into production models has been emphasized by many researchers. Yield randomness, especially in semiconductor manufacturing, poses a mojor challenge for production planning and control. The task becomes even more difficult if the demand for final product is uncertain. An attempt to meet the demand with a higher level of confidence forces one to release more input in the fabrication line. This leads to excessive work-in-process (WIP) inventories which cause jobs to spend unpredictably longer time waiting for the machines. The result is that it is more difficult to meet demand with exceptionally long cycle time and puts further pressure to increase the safety stocks. Due to this spiral effect, it is common to find that the capital tied in inventory is the msot significant factor undermining profitability. We propose a policy to determine the quantity to be processed at each stage of a multi-stage production system in which the yield at each stage may be random and may need rework.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.5
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• pp.50-55
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• 2000

In this paper, the intelligent demand control system was introduced. This system was composed of Intelligent demand controller, RTU(Remote Terminal Unit) and HMI(Human Machine Interface) program.The main features of developed system meter recommended by KEPCO. Secondly, the developed demand controller had a measurement function, so system could be simplified. Thirdly, network function was implemented to control a distanced loads. With program, the users who was not trained could operate system. Additionally, management based Web was considered to monitor and manage the system remotely. The system performance was proved in a several experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1125-1131
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• 2014

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.2006-2011
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• 2010

The superheater in the thermal power plant makes the wet steam into the dry steam with high temperature and high pressure by using the boiler heat. The dry steam pressure rotates the turbine-generator system. The efficiency and life time of the boiler heavily depends on the steam temperature regulation. The steam temperature can be deviated from the reference by the MW demand of the power plant. It is therefore required that the PI(proportional-integral) controller should be robust against the disturbance such as the MW demand. In this paper, the PI controller with the integral state predictor is proposed and applied to regulate the steam temperature of the superheater, and it is compared with the conventional PI controller operated in the thermal power plant in view of control performance.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.813-825
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• 2019

This paper investigated the effects of a government production and shipment stabilization policy and the improvement plan for vegetables in Korea. In this study, a simulation focused on the changes in farm income for Chinese cabbages & radishes using scenarios. The result shows an increase in farm income according to the government policy. In the short-term, demand and supply are fixed, and the policy effect is positive due to the price support effect of the project. However, for mid- to long-term, the price decreases due to the expansion of supply by business beneficiaries, which is likely to be adversely affected by an unstable supply and demand. This increases the burden of government and local government subsidies. This phenomenon is expected to be amplified as the ratio of production and shipbuilding stabilization programs and items expand. When expanding items and adjusting and setting the percentage of participation in the project, it is desirable to set and operate the appropriate business ratio taking into consideration the demand. To improve the effectiveness of the policy, the following remedies were suggested. First, national supply and demand guidelines should be set up, and the autonomous supply and demand control of the producers should be guided by the different preservation according to the implementation of the supply and demand control obligation. As for detailed equipment, it is necessary to establish the reproduction price for each item, set the base price for each wholesale market, increase the incentive for the producers to participate, and fund a business budget to secure business stability.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.99-108
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• 2007

This paper presents an elastic demand stochastic user equilibrium traffic assignment that could not be easily tackled. The elastic demand coupled with a travel performance function is known to converge to a supply-demand equilibrium, where a stochastic user equilibrium (SUE) is obtained. SUE is the state in which all equivalent path costs are equal, and thus no user can reduce his perceived travel cost. The elastic demand SUE traffic assignment can be formulated based on a dynamic system, which is a means of describing how one state develops into another state over the course of time. Traditionally it has been used for control engineering, but it is also useful for transportation problems in that it can describe time-variant traffic movements. Through the Lyapunov Function Theorem, the author proves that the model has a stable solution and confirms it with a numerical example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.