• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.135-137
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• 2018

다수의 원유 선적항으로부터 여러 정유공장으로 수송된 원유를 정제하여 생산한 제품유를 글로벌 수요시장으로 배분하는 2단계 석유물류네트워크의 최적화는 글로벌 석유 메이저의 중요한 의사결정문제이다. 본 연구는 제품유 시장의 가격 및 수요 변동의 영향을 반영하여 원유 수송, 정제 및 제품유 배분을 최적화하기 위한 석유물류네트워크의 로버스트 추계적 모형을 제시하고 있다. 계산실험은 제품유 시장의 가격 및 수요 변동에 관한 시나리오 기반의 데이터를 사용하여 최적화 모형에 적용하였으며, 그 결과를 바탕으로 제시한 최적화모형의 유용성과 타당성을 검증하여 보고하고 있다.

• Journal of Navigation and Port Research
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• v.43 no.6
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• pp.462-468
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• 2019

Offshore oil production faces several difficulties caused by oil price decline and unexpected changes in the global petroleum logistics. This paper suggests a stochastic model for optimizing the offshore oil production under uncertainty. The proposed model incorporates robust optimization and restricted recourse framework, and uses the lower partial mean as the measure of variability of the recourse profit. Some computational experiments and results based on the proposed model using scenario-based data on the crude oil price and demand under uncertainty are examined and presented. This study would be meaningful in decision-making for the offshore oil production problem considering risks under uncertainty.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1135-1148
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• 2018

Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.99-105
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• 2007

According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily produced on the processes may not be efficiently analyzed by current statistical methodologies (i.e., statistical quality control and statistical process control methodologies) because of the dimensionality associated with many input and response variables. Although a number of statistical methods to handle this situation, there is room for improvement. In order to overcome this limitation, we integrated data mining and robust design approach in this research. We find efficiently the significant input variables that connected with the interesting response variables by using the data mining technique. And we find the optimum operating condition of process by using RSM and robust design approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.129-139
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• 2016

This paper presented a robust ship scheduling model using the quadratic programming problem. Given a set of available carriers under control and a set of cargoes to be transported from origin to destination, a robust ship scheduling that can minimize the mean-variance objective function with the required level of profit can be modeled. Computational experiments concerning relevant maritime transportation problems are performed on randomly generated configurations of tanker scheduling in bulk trade. In the first stage, the optimal transportation problem to achieve maximum revenue is solved through the traditional set-packing model that includes all feasible schedules for each carrier. In the second stage, the robust ship scheduling problem is formulated as mentioned in the quadratic programming. Single index model is used to efficiently calculate the variance-covariance matrix of objective function. Significant results are reported to validate that the proposed model can be utilized in the decision problem of ship scheduling after considering robustness and the required level of profit.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.117-131
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• 2015

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.