• Journal of applied mathematics & informatics
• /
• 제13권1_2호
• /
• pp.261-275
• /
• 2003

In this paper we study two stage problems of stochastic convex programming. Solving the problems is very hard. A L-shaped method for it is given. The implement of the algorithm is simple, so less computation work is needed. The result of computation shows that the algorithm is effective.

• Journal of Electrical Engineering and Technology
• /
• 제12권1호
• /
• pp.53-63
• /
• 2017

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

• 대한수학회보
• /
• 제32권2호
• /
• pp.287-296
• /
• 1995

Wets ([4],[5],[6]) considered single objective linear two-stage programming problem under uncertainty with complete recourse. Artstein, Dupacova, Romisch, Schultz and Wets studied stability of this problem id depth. But in many real world problems to make best decision, we need multiple objective functions. So we consider the following multiple objective two-stage programming problems with complete fixed recourse.

• 대한수학회보
• /
• 제31권2호
• /
• pp.243-252
• /
• 1994

• 한국경영과학회지
• /
• 제22권1호
• /
• pp.1-24
• /
• 1997

In this paper, we have proposed new solution methods to solve TSLP (two-stage stochastic linear programming) problems. One solution method is to combine the analytic center concept with Benders' decomposition strategy to solve TSLP problems. Another method is to apply an idea proposed by Geoffrion and Graves to modify the L-shaped algorithm and the analytic center algorithm. We have compared the numerical performance of the proposed algorithms to that of the existing algorithm, the L-shaped algorithm. To effectively compare those algorithms, we have had computational experiments for seven test problems.

• 한국국방경영분석학회지
• /
• 제31권2호
• /
• pp.28-44
• /
• 2005

The previous studies approach the field artillery fire scheduling problem as deterministic and do not explicitly include information on the potential scenario changes. Unfortunately, the effort used to optimize fire sequences and reduce the total time of engagement is often inefficient as the collected military intelligence changes. Instead of modeling the fire sequencing problem as deterministic model, we consider a stochastic artillery fire scheduling model and devise a solution methodology to integrate possible enemy attack scenarios in the evaluation of artillery fire sequences. The goal is to use that information to find robust solutions that withstand disruptions in a better way, Such an approach is important because we can proactively consider the effects of certain unique scheduling decisions. By identifying more robust schedules, cascading delay effects will be minimized. In this paper we describe our stochastic model for the field artillery fire sequencing problem and offer revised robust stochastic model which considers worst scenario first. The robust stochastic model makes the solution more stable than the general two-stage stochastic model and also reduces the computational cost dramatically. We present computational results demonstrating the effectiveness of our proposed method by EVPI, VSS, and Variances.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.1758-1761
• /
• 1997

The problem of long range capacity expansion planing for chemical processing network under uncertain demand forecast secnarios is addressed. This optimization problem involves capactiy expansion timing and sizing of each chemical processing unit to maximize the expected net present value considering the deviation of net present values and the excess capacity over a given time horizon. A multiperiod mixed integer nonlinear programming optimization model that is both solution and modle robust for any realization of demand scenarios is developed using the two-stage stochastic programming algorithm. Two example problems are considered to illustrate the effectiveness of the model.

• 한국경영과학회지
• /
• 제10권1호
• /
• pp.9-13
• /
• 1985

This paper develops a decomposition method for stochastic programming with a block diagonal structure. Here we assume that the right-hand side random vector of each subproblem is differente each other. We first, transform this problem into a master problem, and subproblems in a similar way to Dantizig-Wolfe's Decomposition Princeple, and then solve this master problem by solving subproblems. When we solve a subproblem, we first transform this subproblem to a Deterministic Equivalent Programming (DEF). The form of DEF depends on the type of the random vector of the subproblem. We found the subproblem with finite discrete random vector can be transformed into alinear programming, that with continuous random vector into a convex quadratic programming, and that with random vector of unknown distribution and known mean and variance into a convex nonlinear programming, but the master problem is always a linear programming.

• 산업공학
• /
• 제10권3호
• /
• pp.179-187
• /
• 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.

• 한국수자원학회논문집
• /
• 제43권2호
• /
• pp.131-138
• /
• 2010

다양화되는 물 수요와 기상 이변 등의 영향으로 극심해지는 가뭄에 대비하여 대체 수자원의 확보는 수자원 연구의 매우 중요한 부분이 되었다. 다양한 대체 수자원 중 하수처리장의 방류수는 양호한 수질과 비교적 예측이 가능한 방류량으로 인해 농업용수나 공업용수 혹은 공공용수를 대체할 안정적인 수원으로 관심의 대상이 되고 있다. 본 연구에서는 하수처리수 재이용을 위해 미래의 불확실한 용수 수요량을 고려한 최소의 공사비를 최적화하는 방법을 이진변수를 가지는 2단계 추계학적 선형계획법을 이용하여 제시하였다. 현재 설계하는 하수처리수 재이용 모형은 미래의 용수 수요량까지 고려하여 설계하여야 한다는 점을 고려하여, 미래에 용수수요가 증가할 경우, 기존의 관에 평행한 다른 관을 추가로 건설할 수 있다고 가정하여 2단계에 걸쳐 공사가 가능한 모형을 구축하였다. 그 결과 미래의 물 사용량까지를 모두 고려하여 현재 큰 직경의 관로를 건설하는 경우와 작은 직경의 관로를 두 번에 걸쳐 건설하는 대안 사이의 비용차이를 고려한 모형이 제안되었으며, 가상의 네트워크에 적용되어 그 적용성을 입증하였다. 제안된 모형은 하수 처리수 재이용 네트워크 계획 시 경제적인 관로 설계를 위한 기본 자료로 활용될 수 있으며, 장기적인 물 공급 계획을 수립할 시 여러 가지 설계 대안들에 대한 비교를 위해도 사용이 가능하다.