• Journal of Advanced Marine Engineering and Technology
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• v.23 no.6
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.3
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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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• 1993.10b
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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.

• Korean Management Science Review
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• v.29 no.2
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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.

• Journal of Korea Water Resources Association
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• v.30 no.3
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• pp.269-278
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• 1997

Operating rules, the basic principle of reservoir operation, are mostly developed from maximum or minimum, mean inflow series so that those rules cannot be used in practical operating situations to estimate the expected benefits or provide the operating policies for uncertainty conditions. Many operating rules based on the deterministic method that considers all operation variables including inflows as known variables can not reflect to uncertainties of inflow variations. Explicit operating rules can be developed for improving the weakness. In this method, stochastic trend of inflow series, one of the reservoir operation variables, can be directly method, the stochastic technique was applied to develop reservoir operating rule. In this study, stochastic dynamic programming using the concepts was applied to develop optimal operating rule for the Chungju reservoir system. The developed operating rules are regarded as a practical usage because the operating policy is following up the basic concept of Lag-1 Markov except for flood season. This method can provide reservoir operating rule using the previous stage's inflow and the current stage's beginning storage when the current stage's inflow cannot be predicted properly.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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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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• v.5 no.2
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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.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.855-862
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• 1998

This paper investigates the impact of the forecast error on performance of a reservoir system for hydropower production. Forecast error is measured as th Root Mean Square Error (RMSE) and parametrically varied within a Generalized Maintenance Of Variance Extension (GMOVE) procedure. A set of transition probabilities are calculated as a function of the RMSE of the GMOVE procedure and then incorporated into a Bayesian Stochastic Dynamic Programming model which derives monthly operating policies and assesses their performance. As a case study, the proposed methodology is applied to the Skagit Hydropower System (SHS) in Washington state. The results show that the system performance is a nonlinear function of RMSE and therefor suggested that continued improvements in the current forecast accuracy correspond to gradually greater increase in performance of the SHS.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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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.