• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.65-75
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• 2008

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Korean Management Science Review
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• v.26 no.3
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• pp.117-131
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• 2009

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Journal of Korea Water Resources Association
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• v.37 no.5
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• pp.353-361
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• 2004

Effective storage in agricultural reservoir has been determined through the reservoir simulation operation based on the water budget analysis. Since each watershed has the native property for runoff, considering the runoff yielding from the basin is feasible to the determination of reservoir effective storage. In this study the stochastic linear programming model considering mainly runoff from watershed has been also formulated to analyze the effective storage of the exiting reservoir. The linear decision rule coupled with chance-constrained model in the linear programming model contributes to reduce the size of linear program model without considering the period of analysis. The Geum-Gang reservoir located in Ansung have been adopted to evaluate the effective storage. It has been shown that the effective storage based on the linear programming model is greater than that based on the water budget analysis. It has been also desired that once the effective storage is obtained through the linear programming model, operation of the reservoir should be performed to check the designed capacity.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.191-193
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• 1989

This paper presents a optimal Var allocation algorithm for minimizing power loss and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A stochastic Linear Programming technique based on chance constrained method is applied to solve the probabilistic constraint. The test result in IEEE-14 Bus model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before Var allocation.

• Smart Structures and Systems
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• v.5 no.1
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• pp.69-79
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• 2009

A non-clipped semi-active stochastic optimal control strategy for nonlinear structural systems with MR dampers is developed based on the stochastic averaging method and stochastic dynamical programming principle. A nonlinear stochastic control structure is first modeled as a semi-actively controlled, stochastically excited and dissipated Hamiltonian system. The control force of an MR damper is separated into passive and semi-active parts. The passive control force components, coupled in structural mode space, are incorporated in the drift coefficients by directly using the stochastic averaging method. Then the stochastic dynamical programming principle is applied to establish a dynamical programming equation, from which the semi-active optimal control law is determined and implementable by MR dampers without clipping in terms of the Bingham model. Under the condition on the control performance function given in section 3, the expressions of nonlinear and linear non-clipped semi-active optimal control force components are obtained as well as the non-clipped semi-active LQG control force, and thus the value function and semi-active nonlinear optimal control force are actually existent according to the developed strategy. An example of the controlled stochastic hysteretic column is given to illustrate the application and effectiveness of the developed semi-active optimal control strategy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.685-688
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• 2004

본 연구에서는 저수지군 연계운영을 위한 표본 추계학적 선형 계획(SSLP, Sampling Stochastic Linear Programming) 모형을 제안한다. 일반적 추계학적 모형은 과거 자료로부터 확률변수의 확률분포를 추정하고 이를 몇 개 구간으로 나누어 이산 확률 값을 산정하여 기댓값이 최대가 되는 운영방안을 도출하지만 저수지 유입량 예측시 고려되어야할 지속성 효과(Persistemcy Effect)와 유역간 또는 시점별 공분산 효과(The joint spatial and temporal correlations)를 반영하는데 많은 한계가 있다. 이를 극복하기 위하여 과거자료 자체를 유입량 시나리오로 적용하여 시${\cdot}$공간적 상관관계를 유지하는 표본 추계학적(Sampling Stochastic)기법을 바탕으로 Simple Recourse Model로 구성한 추계학적 선형 계획 모형을 제시한다. 이 모형은 미국 기상청(NWS)에서 발생 가능한 유입량의 시나리오를 예측하는 방법인 앙상블 유량 예측(ESP, Ensemble Streamflow Prediction)을 통한 시나리오를 적용함으로써 좀더 신뢰성 있는 저수지군 연계운영 계획을 도출 할 수 있을 것으로 기대된다.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.35-49
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• 1981

This paper is concerned with developing a Multi-period Behavioral Model for the portfolio selection problem. The unique feature of the model is that it treats a number of factors and decision variables considered germane in decision making on an interrelated basis. The formulated problem has the structure of a Chance Constrained programming Model. Then empoloying arguments of Central Limit Theorem and normality assumption the stochastic model is reduced to that of a Non-Linear Programming Model. Finally, a number of interesting properties for the reduced model are established.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.525-529
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• 1996

Decision environments involve a high degree of uncertainty as well as multiple, conflicting goals. Although traditional goal programming offers a means of considering multiple, conflicting goals and arrives at a satisficing solution in a deterministic manner, its major drawback is that decision makers often specify aspiration level of each goal as a single number. To overcome the problem of setting aspiration levels, chance constrained programming can be incorporated into goal programming formulation so that sampling information can be utilized to describe uncertainty distribution. Another drawback of goal programming is that it does not provide a systematic approach to set priorities and trade-offs among conflicting goals. To overcome this weekness, the analytic hierarchy process(AHP) is used in the model. Also, most goal programming models in the literature are of a linear form, although some nonlinear models have been presented. Consideration of risk in technological coefficients and right hand sides, however, leads to nonlinear goal programming models, which require a linear approximation to be solved. In this paper, chance constrained reformulation with linear approximation is presented for a 0-1 goal programming problem whose technological coefficients and right hand sides are stochastic. The model is presented with a numerical example for the purpose of demonstration.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.863-865
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• 1988

This paper presents a optimal Var allocation algorithm for minimizing transmission line losses and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A Stocastic Linear programming technique based on chance constrained method is applied, to solve the var allocation problem with probabilistic constraint. The test result in 6-Bus Model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before var allocation.

• IE interfaces
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• v.10 no.3
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• pp.179-187
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• 1997

The efficiency of material flow systems in terms of optimal network flow and minimum cost flow has always been an important design and operational goal in material handling and distribution system. In this research, an attempt was made to develop a new algorithm and the model to solve a stochastic material flow network with bidirectional and uncertain flows. A stochastic material flow network with bidirectional flows can be considered from a finite set with unknown demand probabilities of each node. This problem can be formulated as a special case of a two-stage linear programming problem which can be converted into an equivalent linear program. To find the optimal solution of proposed stochastic material flow network, some terminologies and algorithms together with theories are developed based on the partitioning and subgradient techniques. A computer program applying the proposed method was developed and was applied to various problems.