• 자원ㆍ환경경제연구
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• 제9권5호
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• pp.925-945
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• 2000

This paper analyses the impact of energy-saving investment on Greenhouse gas emissions using a model of energy demand in Korea. SUR method was employed to estimate the demand equation. The econometric estimates provide information about the energy price divisia index, sector income, and energy saving-investment elasticities of energy demand. Except for energy price divisia, the elasticities of each variable are statistically significant. Also, the price and substitution elasticities of each energy price are similar to the results reported by the previous studies. The energy-saving investment is statistically significant and elasticities of each sector is inelastic. Using the coefficient of energy-saving investment and carbon transmission coefficient, the amount of reduction of energy demand and the reduction of carbon emissions can be estimated. The simulation is performed with the scenario that the energy-saving investment increase by 10~50%, keeping up with Equipment Investment Plan of 30% increase in energy-saving investment by 2000. The results show that the reduction of energy demand measured as 11.2% based upon 1995's level of the energy demand, in industrial sector. Accordingly, the carbon emissions will be reduced by 11.3% based upon 1995's level of the carbon emissions in industrial sector.

• Industrial Engineering and Management Systems
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• 제13권2호
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• pp.129-147
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• 2014

This study proposes an optimal operational policy for a green supply chain (GSC) where a retailer pays an incentive for collection of used products from customers and determines the optimal order quantity of a single product under uncertainty in product demand. A manufacturer produces the optimal order quantity of product using recyclable parts with acceptable quality levels and covers a part of the retailer's incentive from the recycled parts. Here, two scenarios for the product demand are assumed as: the distribution of product demand is known, and only both mean and variance are known. This paper develops mathematical models to find how order quantity, collection incentive of used products and lower limit of quality level for recycling affect the expected profits of each member and the whole supply chain under both a decentralized GSC (DGSC) and an integrated GSC (IGSC). The analysis numerically compares the results under DGSC with those under IGSC for each scenario of product demand. Also, the effect of the quality of the recyclable parts on the optimal decisions is shown. Moreover, supply chain coordination to shift the optimal decisions of IGSC is discussed based on: I) profit ratio, II) Nash bargaining solution, and III) Combination of (I) and (II).

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.343-348
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• 2002

We are concerned with a long-term replenishment contract for the ARIMA demand process in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows us to contract future replenishments at a time with a price discount. Due to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand process, we present a model for the contract and an algorithm to find the number of the future replenishments. Numerical experiments show that the proposed algorithm is efficient and accurate.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.441-445
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• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• 대한안전경영과학회지
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• 제9권3호
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• pp.119-125
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• 2007

As competition in manufacturing enterprise is contested, the scope of safely production planning, manufacturing enterprise should ensure, has been reduced. The more upstream of SCM, the more reduction of scope of production planning. As a result, order fluctuation is more sharply contested. Through improving the logistics network, it is best way that the end user's demand information is conveyed to upstream of SCM, but it is difficult in fact. In this paper, it mention the way of robustic adjustment, in the suppliers' point of view, the end user's demand information is dammed up, as they postpone responding the customer's order as a possible. And it will show the result of appling the way, as a case study.

• 산업경영시스템학회지
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• 제32권3호
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• pp.93-98
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• 2009

This paper studies that $\sigma$ values(10, 20, 50) and $\rho$ values(-0.6, -0.3, 0.0, 0.3, 0.6) affect the total inventory cost of a supply chain and order fill rate when the market demand process follows a general auto-correlated AR(1) process without seasonality. $\sigma$ indicates the degree of demand fluctuation and $\rho$ means the trend of demand. ANOVA tests using a 5% significance level are performed in SPSS to examine significant performance changes among various cases.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1990년도 춘계공동학술대회논문집; 한국과학기술원; 28 Apr. 1990
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• pp.125-139
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• 1990

This paper presents a forecasting method for domestic demand of electric home appliances. Because of lack of data, some popular methods such as time series analysis may not be appropriate to forecast such a demand domestically. We suggest a systematic and practical method by considering structural parameters and variables which determine the actual demand. We use this model to forecast the demand of color TV. Since the parameters in our model may be variant according to the change of economic environment, our model leads the user to develop a dynamic model to be used in the well-known System Dynamics Approach.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2006년도 춘계 국제학술대회 논문집
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• pp.235-240
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• 2006

The existing inventory managements bear a relation to forecasting or assumptions. So these methods become more complicated and more expensive systems as time goes. This paper developed a practical inventory system which is called DCC(demand control chart). DCC does not 'forecast' but 'control' the trend of demand without assumptions. According to the trend of sales, DCC adjusts an order quantity considering the capacity of shelf in a store. Specially, DCC is a useful method under FRID system. Besides, this paper introduces EPFR(Every Period Full Replenishment) policy for reducing stocks.

• 한국디지털건축인테리어학회논문집
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• 제11권3호
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• pp.17-25
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• 2011

The purpose of this study is basic research for demand forecasting of residence which will form in the new industrial complex. Our industrial complexes are important base of National economic development until 90's. But The industry complexes which confront facility which is old, Maintenance negligence and change of industrial system must change new shape from existing function of industrial complexes. The results of this study are that the present housing using condition are different at ages and Using intentions of staff facility are negative generally. These means that When starting the structure hightening, the utility system provision and public information of residence facilities in industral complex are necessary for workers. So, The key points of industry complex structure hightening are that the accurate recognition about demand and the development of financial support policy for young Income low-end workers do to make advance efficiently.

• 산업경영시스템학회지
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• 제43권2호
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.