• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.2
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• pp.45-56
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• 2012

This paper considers a supply chain consisting of a component manufacturer and a product manufacturer. The component manufacturer provides components for the product manufacturer based on a vendor-managed inventory type of supply contract, and also faces demands from the market with the option of to accept or reject each incoming demand. Using the Markov decision process model, we examine the structure of the optimal production control and inventory rationing policy. Two types of heuristics are presented. One is the fixed-buffer policy and the other uses two linear functions. We implement a computational study and present managerial insights based on the observations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1758-1761
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• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.27-34
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• 1995

In this paper, we have developed a model to determine the input 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 reworking at this stage. Yield randomness. especially in a semiconductor industry, is a most challenging problem for production control. The demand for flnal product is uncertain. We have extended the model proposed in Park and Kim[9] to consider a multiple number of reworkings which can be done at any stage prior to or tat the stage whose output in bad, depending on the level of the defect.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.714-723
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• 1999

This paper presents a short-term scheduling algorithm for the operation of steelmaking processes. The scope of the problem covers refining of the hot iron transferred form a blast furnace, ladle treatment, continuous casting, hot-rolling, and coiling for the final products that should satisfy the given demand. The processing time at each unit depends on how much the batch amount is treated, and te dedicated intermediate storage with finite capacity between the units is considered. Resource constraints and initial amount of each state are incorporated into the presented scheduling model for the algorithm of on-line scheduling. We propose amixed integer linear programming (MILP) model with two objectives for the scheduling. The first is to maximize the total profit while atisfying the due date constraint for each product. And the second is to minimize the total processing time, makespan, while satisfying the demand for each product. Especially, we observe the effect of penalizing the intermediate storage and the inventory level of the final product on the scheduling results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.399-409
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• 2006

This paper presents a novel method for satisfying the thermal comfort of indoor environment and reducing the summer peak demand power by minimizing the power consumption for an Air-conditioner within a space. Korea Electric Power Corporation (KEPCO) use the fixed duty cycle control method regardless of the indoor thermal environment. However, this method has disadvantages that energy saving depends on the set-point value of the Air-Conditioner and direct load control (DLC) has no net effects on Air-conditioners if the appliance has a lower operating cycle than the fixed duty cycle. In this paper, the variable duty cycle control method is proposed in order to compensate the weakness of conventional fixed duty cycle control method and improve the satisfaction of residents and the reduction of peak demand. The proposed method estimates the predict mean vote (PMV) at the next step with predicted temperature and humidity using the back propagation neural network model. It is possible to reduce the energy consumption by maintaining the Air-conditioner's OFF state when the PMV lies in the thermal comfort range. To verify the effectiveness of the proposed variable duty cycle control method, the case study is performed using the historical data on Sep. 7th, 2001 acquired at a classroom in Seoul and the obtained results are compared with the fixed duty cycle control method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2606-2611
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• 2003

Comprehensive study on the control system design for a RTP process has been conducted. The purpose of the control system is to maintain maximum temperature uniformity across the silicon wafer achieving precise tracking for various reference trajectories. The study has been carried out in two stages: thermal balance modeling on the basis of a semi-empirical radiation model, and optimal iterative learning controller design on the basis of a linear state space model. First, we found through steady state radiation modeling that the fourth power of wafer temperatures, lamp powers, and the fourth power of chamber wall temperature are related by an emissivity-independent linear equation. Next, for control of the MIMO system, a state space modeland LQG-based two-stage batch control technique was derived and employed to reduce the heavy computational demand in the original two-stage batch control technique. By accommodating the first result, a linear state space model for the controller design was identified between the lamp powers and the fourth power of wafer temperatures as inputs and outputs, respectively. The control system was applied to an experimental RTP equipment. As a consequence, great uniformity improvement could be attained over the entire time horizon compared to the original multi-loop PID control. In addition, controller implementation was standardized and facilitated by completely eliminating the tedious and lengthy control tuning trial.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.53-62
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• 2009

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size. During the two periods, the retailer faces stochastic (truncated Poisson distributed) demands and he/she places orders to meet the demands. The manufacturer provides for the retailer a price discount for the second period order if its quantity exceeds the first period order quantity. In this paper we extend the above two-period model to a k-period one (where k < 2) and propose a stochastic nonlinear mixed binary integer program for it. In order to make the program tractable, the nonlinear term involving the sum of truncated Poisson cumulative probability function values over a certain range of demand is approximated by an i-interval piecewise linear function. With the value of i selected and fixed, the piecewise linear function is determined using an evolutionary algorithm where its fitness to the original nonlinear term is maximized. The resulting piecewise linear mixed binary integer program is then transformed to a mixed binary integer linear program. With the k-period model developed, we suggest a solution procedure of receding horizon control style to solve n-period (n < k) order decision problems. We implement Lee's two-period model and the proposed k-period model for the use in receding horizon control style to solve n-period order decision problems, and compare between the two models in terms of the pattern of order quantities and the total profits. Our computational study shows that the proposed model is superior to the two-period model with respect to the total profits, and that order quantities from the proposed model have higher fluctuations over periods.

• Journal of Korea Water Resources Association
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• v.40 no.4
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• pp.325-334
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• 2007

In this study, a long-term unsteady simulation model has been developed using rigid water column theory which is more accurate than Extended-period model and more efficient comparing with water-hammer simulation model. The developed model is applied to 24-hours unsteady simulation considering daily water-demand and water-hammer analysis caused by closing a valve. For the case of 24-hours daily simulation, the pressure of each node decreases as the water demand increase, and when the water demand decrease, the pressure increases. During the simulation, the amplitudes of flow and pressure variation are different in each node and the pattern of flow variation as well as water demand is quite different than that of KYPIPE2. Such discrepancy necessitates the development of unsteady flow analysis model in water distribution network system. When the model is applied to water-hammer analysis, the pressure and flow variation occurred simultaneously through the entire network system by neglecting the compressibility of water. Although water-hammer model shows the lag of travel time due to fluid elasticity, in the aspect of pressure and flow fluctuation, the trend of overall variation and quantity of the result are similar to that of water-hammer model. This model is expected for the analysis of gradual long-term unsteady flow variations providing computational accuracy and efficiency as well as identifying pollutant dispersion, pressure control, leakage reduction corresponding to flow-demand pattern, and management of long-term pipeline net work systems related with flowrate and pressure variation in pipeline network systems

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.

• Journal of the military operations research society of Korea
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• v.3 no.2
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• pp.79-89
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• 1977

The purpose of this thesis is to overcome the shortcomings of the existing system which lacks cost-conciousness and does not consider the essentiality or the importance of an item, and to seek the method of providing effective, efficient and economic supply which is the objective of the military inventory management. Selective management technique and lot size model whose demand, and order and shipping time are distributed, are intorduced, and required distributions and parameters are analyzed. Finally hypothetical data are utilized to obtain the model output, which are compared with the existing model and analyzed.