• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.9-17
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• 1990

This paper deals with a FOQ( ; fixed-order quantity) model for spare-part inventory control. In a spare-part inventory problem, stock depletion arises not from external market demand but from internal demand resulting from failures of parts in use. The problem differs from the classical inventory problem in that the demand for a failed part never arises more during stockout period, since the unit remains inoperative when stockout occurs until the failed part is replaced by new one. In the problem under consideration, n identical units are operating simultaneously and failed one is replaced immediately by new one if on-hand spares remain. In order to replenish spares, an order with quantity Q is placed whenever the number of on-hand spares falls to levels. The average annual cost of operating the spare-part inventory system is derived under the assumption that both lifetime of a part and replenishment lead-time distributions are exponential.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.10
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• pp.381-386
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• 2016

An optimization algorithm is developed based on a simulation case of a single family house model equipped with PV arrays. To increase the nationwide use of PV power generation facilities, a market-competitive electricity price needs to be introduced, which is determined based on the time of use. In this study, quadratic programming optimization was applied to minimize the electricity bill while maintaining the indoor temperature within allowable error bounds. For optimization, it is assumed that the weather and electricity demand are predicted. An EnergyPlus-based house model was approximated by using an equivalent RC circuit model for application as a linear constraint to the optimization. Based on the RC model, model predictive control was applied to the management of the cooling load and electricity for the first week of August. The result shows that more than 25% of electricity consumed for cooling can be saved by allowing excursions of temperature error within an affordable range. In addition, profit can be made by reselling electricity to the main grid energy supplier during peak hours.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.4
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• pp.775-787
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• 2014

Availabilities of subsea Blowout Preventers (BOP) in the Gulf of Mexico Outer Continental Shelf (GoM OCS) is investigated using a Markov method. An updated ${\beta}$ factor model by SINTEF is used for common-cause failures in multiple redundant systems. Coefficient values of failure rates for the Markov model are derived using the ${\beta}$ factor model of the PDS (reliability of computer-based safety systems, Norwegian acronym) method. The blind shear ram preventer system of the subsea BOP components considers a demand rate to reflect reality more. Markov models considering the demand rate for one or two components are introduced. Two data sets are compared at the GoM OCS. The results show that three or four pipe ram preventers give similar availabilities, but redundant blind shear ram preventers or annular preventers enhance the availability of the subsea BOP. Also control systems (PODs) and connectors are contributable components to improve the availability of the subsea BOPs based on sensitivity analysis.

• Land and Housing Review
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• v.2 no.3
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• pp.217-226
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• 2011

The Seoul Metropolitan Government is striving to minimize the amount of traffic according to the supply of apartment houses along with the solution of housing shortage for the low income people through high density development near the subway area. Therefore, a stronger policy is necessary to control the traffic of the passenger cars in a subway area for the successful high density development focusing on public transportation, and especially, the estimation of the demand of cars with high reliability is necessary to control the demand of parking such as the limited supply of parking lot. Accordingly, this study developed car ownership forecasting model using Look-up Table among category analyses which are easy to be applied and have high reliability. The estimation method using Look-up-Table is possible to be applied to both measurable and immeasurable types, easy to accumulate data, and features the flexible responding depending on the changes of conditions. This study established Look-up-Table model through the survey of geographical location, the scale of housing, the accessible distance to a subway station and to a bus station, the number of bus routes, and the number of car owned with data regarding 242 blocks in Seoul City as subjects.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.559-571
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• 2010

The Demand-Control model has been one of the most popular theoretical models to explain job stress. This study extends the Demand-Control model to the team level and examines the relationship between job demand and job stress to tests the moderating effect of the `team relationship climate' on the relationship between job demand and job stress. Data were collected from 34 teams across 19 organizations and analyzed using HLM. The results showed that job demand was significantly related to job stress. Based on the team level analysis, the team relationship climate was found to moderate the relationship between job demand and job stress. In addition, the consideration behavior by the leader was significantly correlated with the team relationship climate. Finally the theoretical and practical implications and limitations of this study were discussed.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.217-229
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• 2013

As customers' demands for diversified small-quantity products have been increased, there have been great efforts for a firm to respond to customers' demands flexibly and minimize the cost of inventory at the same time. To achieve that goal, in SCM perspective, many firms have tried to control the inventory efficiently. We present an mathematical model to determine the near optimal (s, S) policy of the supply chain, composed of multi suppliers, a warehouse and multi retailers. (s, S) policy is to order the quantity up to target inventory level when inventory level falls below the reorder point. But it is difficult to analyze inventory level because it is varied with stochastic demand of customers. To reflect stochastic demand of customers in our model, we do the analyses in the following order. First, the analysis of inventory in retailers is done at the mathematical model that we present. Then, the analysis of demand pattern in a warehouse is performed as the inventory of a warehouse is much effected by retailers' order. After that, the analysis of inventory in a warehouse is followed. Finally, the integrated mathematical model is presented. It is not easy to get the solution of the mathematical model, because it includes many stochastic factors. Thus, we get the solutions after the stochastic demand is approximated, then they are verified by the simulations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.6
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.97.2-97
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• 2001

This work is concerned with the improvement of the productivity and the product quality in the polymerization reactors by using model-on-demand predictive control(MoDPC). This technique is applied to a continuous styrene polymerization reactor and a semibatch methyl methacrylate (MMA)/vinyl acetate(VAc) copolymerization reactor. The regress is constructed with the most influential variables the conversion and the jacket inlet temperature for the styrene polymerization reactor, and the free volume and the reactor temperature for the MMA/VAc copolymerization reactor through open loop operations. From the simulation results for setpoint tracking and disturbance rejection problems, it is demonstrated that the MoDPC shows ...

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.181-186
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• 2000

Recently a strong request for the improvement in irrigation water management in order to flexibly meet the spacial and time changes of water demand for agricultural and other uses by saving agricultural water. Thereby, the purpose of this study is to design of Basin Water Management Program(BWMP). BWMP is operate with Open Control System. Accordingly, BMWP is easy to acquire data and control irrigation and drainage facilities. BWMP are consist of Data Base Management System(DBMS) and Model System. DBMS make it possible to analyze data related with planing for water schedul and establish database. Model System are calculate reservoir inflow, reservoir effluent and basin water demand. Finally, operator is decide reservoir operation in consider of Model System and DBMS. BWMP might be nicely adapted to the planning and decision for saving water.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.484-492
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• 2016

Smart grid (SG) technology is now elevating the conventional power grid system to one that functions more cooperatively, responsively, and economically. When applied in an SG the demand side management (DSM) technique can improve its reliability by dynamically changing electricity consumption or rescheduling it. In this paper, we propose a new SG scheduling scheme that uses the DSM technique. To achieve effective SG management, we adopt a mixed pricing strategy based on the Rubinstein-Stahl bargaining game and a repeated game model. The proposed game-based pricing strategy provides energy routing for effective energy sharing and allows consumers to make informed decisions regarding their power consumption. Our approach can encourage consumers to schedule their power consumption profiles independently while minimizing their payment and the peak-to-average ratio (PAR). Through a simulation study, it is demonstrated that the proposed scheme can obtain a better performance than other existing schemes in terms of power consumption, price, average payment, etc.