• Journal of Advanced Marine Engineering and Technology
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• 제23권6호
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• pp.785-793
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• 1999

There are two kinds of method in ship maintenance. One is the corrective maintenance and the other is the preventive maintenance. For these maintenances recently the stochastic techniques are widely used to keep the maximum availibility and the optimal maintenance period minimizing a given cost function. Thus this paper suggest a method to decide the optimal policy of ship's maintenances by using dynamic programming and the effectiveness of the method is verified through several examples in which failure rates and maintenance data of ship's machineries and parts are given.

• 대한산업공학회지
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• 제31권3호
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• pp.194-200
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• 2005

A stochastic dynamic lot sizing problem for multi-item is suggested in the case that the distribution of the cumulative demand is known over finite planning horizons and all unsatisfied demand is fully backlogged. Each item is produced simultaneously at a variable ratio of input resources employed whenever setup is incurred. A dynamic programming algorithm is proposed to find the optimal production policy, which resembles the Wagner-Whitin algorithm for the deterministic case problem but with some additional feasibility constraints.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.155-159
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• 1993

In this paper, we consider the problem of a stochastic optimal switching control, which can be applied to the control of a system with uncertain demand such as a control problem of a power plant. The dynamic programming method is applied for the formulation of the optimal control problem. We solve the system of Quasi-Variational Inequalities(QVI) using an algoritlim which involves the finite difference approximation and contraction mapping method. A mathematical example of the optimal switching control is constructed. The actual performance of the algorithm is also tested through the solution of the constructed example.

• 경영과학
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• 제29권2호
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• pp.91-103
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• 2012

Revenue management problems originated in the 1970's in the context of the airline industry have been successfully introduced in airline industries. It has started on the capacity control by booking classes for available seats, and has been recognized as a powerful tool to maximize the total revenue. Changing customer behavior and airline market environments, however, has required a new mechanism for improving the revenue. Dynamic pricing is one of innovative tools which is to adjust prices according to the market status. In this paper, we consider a dynamic pricing and seat control problem for discrete time horizon. The problem can be modeled as a stochastic programming problem. Applying the linear approximation technique and given the price set for each time, we suggest a mixed Integer Programming model to solve our problem efficiently. From the simulation results, we can find our model makes good performance and can be expanded to other comprehensive problems.

• 한국수자원학회논문집
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• 제30권3호
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• pp.269-278
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• 1997

저수지 운영의 기초가 되는 운영률은 대부분 과거기록 유입량중 최대 혹은 최소의 극한치 자료를 이용하거나 평균치 자료를 이용하여 도출하기 때문에 실제 운영에서 발생할 수 있는 불확실성에 대처한 기대편익 산정이나 운영방안 수립에는 적절히 이용할 수 없다. 또한 지금까지 개발된 대부분의 운영률은 유입량을 포함하여 모든 운영변수를 이미 알고 있다는 확정론적 방법에 기초하고 있어 유입량의 불확실성을 반영하지 못하는 단점이 있다. 이를 개선할 수 있는 방법으로 추계학 분석기법에 의한 운영률을 개발할 수 있는데 이는 저수지 상태방정식의 구성요소인 유입량의 추계학적 특성을 시계열상에서 이산화된 천이확률로 처리하여 모형에 적용할 수 있다. 확정론적 방법에 의한 저수지 운영방안을 개선시키기 위하여 추계학적 방법에 의한 저수지 운영률을 개발하였다. 본 연구에서는 이와같은 방법론에 따른 양해 추계학적 동적계획기법을 이용하여 충주 저수지 시스템의 최적 운영 방안을 마련하였다. 개발된 운영률을 홍수기를 제외하고는 Lag-1 Markov 모형의 기본가정을 충실히 따르고 있어 저수지 운영률로의 이용이 가능하며, 운영단계의 유입량을 적절히 예측할 수 없는 현실에서 전단계의 유입량과 적용단계의 저류량만을 이용하는 저수지 운영률의 개발이 가능하다.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.183-199
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• 2007

In this paper we research the problem in which the objective is to minimize the sum of squared deviations of job expected completion times from the due date, and the job processing times are stochastic. In the problem the machine is subject to stochastic breakdowns and all jobs are preempt-repeat. In order to show that the replacing ESSD by SSDE is reasonable, we discuss difference between ESSD function and SSDE function. We first give an express of the expected completion times for both cases without resampling and with resampling. Then we show that the optimal sequence of the problem V-shaped with respect to expected occupying time. A dynamic programming algorithm based on the V-shape property of the optimal sequence is suggested. The time complexity of the algorithm is pseudopolynomial.

• Water Engineering Research
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• 제5권2호
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• pp.101-109
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• 2004

Aggregating information by combining forecasts from two or more forecasting methods is an alternative to using forecasts from just a single method to improve forecast accuracy. This paper describes the development and use of a monthly inflow forecast model based on an optimal linear combination (OLC) of forecasts derived from naive, persistence, and Ensemble Streamflow Prediction (ESP) forecasts. Using the cross-validation technique, the OLC model made 1-month ahead probabilistic forecasts for the Chungju multi-purpose dam inflows for 15 years. For most of the verification months, the skill associated with the OLC forecast was superior to those drawn from the individual forecast techniques. Therefore this study demonstrates that OLC can improve the accuracy of the ESP forecast, especially during the dry season. This study also examined the value of the OLC forecasts in reservoir operations. Stochastic Dynamic Programming (SDP) derived the optimal operating policy for the Chungju multi-purpose dam operation and the derived policy was simulated using the 15-year observed inflows. The simulation results showed the SDP model that updated its probability from the new OLC forecast provided more efficient operation decisions than the conventional SDP model.

• 한국수자원학회논문집
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• 제31권6호
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• pp.855-862
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• 1998

본 연구는 수력발전을 위한 저수지 관리에 있어 예측오차의 영향을 살펴보기 위해 예측오차를 Root Mean Square Error(RMSE)로 측정하였고, 이를 Generalized Maintenance Of Variance Extension (GMOVE)기법을 통하여 변화시켜보았다.변화된 예측오차의 RMSE는 천이확률을 통하여 Bayesian Stochastic Dynamic Programming (BSDP)에 고려되어졌으며, 이 BSDP 모형을 이용하여 월별 방류량을 결정하였고 그 유용성을 평가하였다. 제시된 연구방법은 미국의 Skagit 시스템에 적용되었고, 그 결과로 Skagit 시스템의 운영은 예측오차의 RMSE에 비선형이므로 반응하므로 이 시스템의 운영을 개선하기 위해서는 현재의 수문학적 예측기법을 개선해야함을 제시하였다.

• 한국경영과학회지
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• 제26권3호
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• pp.105-117
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• 2001

This paper models supply uncertainty in the dynamic Newsboy problem context. The system consists of one supplier and one retailer who places an order to the supplier every period to meet stochastic demand. Supply uncertainty is modeled as the uncertainty in quantities delivered by the supplier. That is, the supplier delivers exactly the amount ordered by the retailer with probability of $\beta$ and the amount minus K with probability of (1-$\beta$). We formulate the problem as a dynamic programming problem and prove that retailer’s optimal replenishment policy is a stationary base-stock policy. Through a numerical study, we found that the cost increase due to supply uncertainty is significant and that the costs increase more rapidly as supply uncertainty increases. We also identified the effects of various system parameters. One of the interesting results is that as retailer’s demand uncertainty, the other uncertainty in our model, increases, the cost increase due to supply uncertainty becomes less significant.

• 대한산업공학회지
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• 제41권3호
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• pp.259-266
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• 2015

Among many energy resources, natural gas has recently received a remarkable amount of attention, particularly from the electrical generation industry. This is in part due to increasing shale gas production, providing an environment-friendly fossil fuel, and high risk of nuclear power. Because South Korea, the world's second largest LNG importing nation after Japan, has no international natural gas pipelines and relies on imports in the form of LNG, the natural gas has been traditionally procured by long term LNG contracts at relatively high price. Thus, there is a need of developing an Asian LNG trading hub, where LNG can be traded at more competitive spot prices. In a natural gas spot market, the amount of natural gas to be bought should be carefully determined considering a limited storage capacity and future pricing dynamics. In this work, the problem to find the optimal amount of natural gas in a spot market is formulated as a Markov decision process (MDP) in risk neutral environment and the optimal base stock policy which depends on a stage and price is established. Taking into account price and demand uncertainties, the basestock target levels are simply approximated from dynamic programming. The simulation results show that the basestock policy can be one of effective ways for procurement of LNG in a spot market.