• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.16-21
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• 2005

In this study, a new Unit Commitment (UC) algorithm is proposed to consider the uncertainty of a daily load profile. The proposed algorithm calculates the UC results with a lower load level than that generated by the conventional load forecast method and the greater hourly reserve allocation. In case of the worst load forecast, the deviation of the conventional UC solution can be overcome with the proposed method. The proposed method is tested with sample systems, which indicates that the new UC algorithm yields a completely feasible solution even when the worst load forecast is applied. Also, the effects of the uncertain hourly load demand are statistically analyzed, particularly by the consideration of the average over generation and the average under generation. Finally, it is shown that independent power producers participating in electricity spot-markets can establish bidding strategies by means of the statistical analysis. Therefore, it is expected that the proposed method can be used as the basic guideline for establishing bidding strategies under the deregulation power pool.

• Korean System Dynamics Review
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• v.13 no.1
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• pp.81-112
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• 2012

This study investigate the impact of supply chain contracts on supply chain performance. This study employed Price adjustment contract(PAC) and Quantity adjustment contract(QAC) as two main types of a vertical coordination mechanism. We simulate different types of coordination mechanisms with various degrees of demand uncertainties and several capacity tightness scenarios. This study shows that PAC and QAC significantly enhance the supply chain profits and fill rates suggesting that supply chain performance can be improved by implementing a proper coordination mechanism depends on the level of a capacity tightness and demand uncertainty.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.51-58
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• 1991

This paper presents a single-echelon, single item, stochastic lead time and static demand inventory model for situations in which, during the stockout period, a fraction ${\beta}$ of the demand is backordered and the remaining fraction $(1-{\beta})$ is lost. In this situations, an objective function representing the average annual cost of inventory system is obtained by defining a time-proportional backorder cost and a fixed penalty cost per unit lost. The optimal operating policy variables minimizing the average annual cost are calculated iteratively. At the extremet ${\beta}=1$, the model presented reduces to the usual backorder case. A numerical example is solved to illustrate the algorithm developed.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.99-107
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• 2007

We consider (worst-case) robust optimization versions of the Economic Order Quantity (EOQ) model with decreasing cost functions. Two variants of the EOQ model are discussed, in which the purchasing costs are decreasing power functions in either the order quantity or demand rate. We develop the corresponding worst-case robust optimization models of the two variants, where the parameters in the purchasing cost function of each model are uncertain but known to lie in an ellipsoid. For the robust EOQ model with the purchasing cost being a decreasing function of the demand rate, we derive the analytical optimal solution. For the robust EOQ model with the purchasing cost being a decreasing function of the order quantity, we prove that it is a convex optimization problem, and thus lends itself to efficient numerical algorithms.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• 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.

• Management Science and Financial Engineering
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• v.6 no.2
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• pp.1-28
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• 2000

Supply chain management issues faced by a manufacturing company are considered in this paper. The supply chain consists of a manufacturing company and its suppliers. The manufacturer produces multiple products with inputs (e.g., raw materials) from the suppliers, but each product needs a different mix of these inputs. The market demand for the products is uncertain. We develop a mathematical model and algorithm, which can help the manufacturer to solve its procurement decision problem: how much of raw material to order from which supplier. The model incorporates such factors as market demand uncertainty, product's input requirement, supplier's as well as manufacturer's capacity, plus other costs comparable with those in a typical newsboy problem. Numerical examples are presented to see the interacting effects among critical parameters and variables.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.208-211
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• 2009

To reduce the peak demand the promotion of gas cooling(absorption chillers and GHPs) is required. In this study the effect of electric power peak-cut has been analyzed using two methods. One is based on monthly LNG consumption data and the other is using the gas cooling capacity installed. Both methods agreed well with each other within the uncertainty of 20%. It was found that the gas cooling had the peak cut effect of 1,500-2,000 MW for recent 5 years (2003 - 2007). The ratio of gas cooling to the whole cooling demand was 12-15%, which is needed to be increased.

• Journal of the Korean Operations Research and Management Science Society
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• v.4 no.1
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• pp.59-64
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• 1979

The application of optimization techniques to the planning of industrial, economic, administrative and military activities with random technological coefficients has been extensively studied in the literature. Stochastic (linear) programs with simple recourse essentially model the allocation of scarce resources under uncertainty with linear penalties associated with shortages or surplus. This work on a problem with a discrete random resource vector, "The allocation of aircraft under uncertain demand" given in (1), is easily and efficiently handled by the application of the recently developed Wets' algorithm (8) for solving stochastic programs with simple recourse, which approves that such class of stochastic problems can be solved with the same efficiency as solving linear programs of the same size. It is known that the algorithm is also applicable to stochastic programs with continuous random demands for their approximate solutions.

• Earthquakes and Structures
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• v.2 no.1
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• pp.25-42
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• 2011

This paper discusses a mechanical model for the vulnerability assessment of old masonry building aggregates that takes into account the uncertainties inherent to the building parameters, to the seismic demand and to the model error. The structural capacity is represented as an analytical function of a selected number of geometrical and mechanical parameters. Applying a suitable procedure for the uncertainty propagation, the statistical moments of the capacity curve are obtained as a function of the statistical moments of the input parameters, showing the role of each one in the overall capacity definition. The seismic demand is represented by response spectra; vulnerability analysis is carried out with respect to a certain number of random limit states. Fragility curves are derived taking into account the uncertainties of each quantity involved.