• 대한수학회논문집
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• 제28권2호
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• pp.419-423
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• 2013

In this paper we present a robust duality theory for generalized convex programming problems under data uncertainty. Recently, Jeyakumar, Li and Lee [Nonlinear Analysis 75 (2012), no. 3, 1362-1373] established a robust duality theory for generalized convex programming problems in the face of data uncertainty. Furthermore, we extend results of Jeyakumar, Li and Lee for an uncertain multiobjective robust optimization problem.

• 산업경영시스템학회지
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• 제20권41호
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• pp.41-50
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• 1997

In this paper, we presents the method for finding the compensatory solution for fuzzy multiobjective nonlinear programming problem with fuzzy parameters involved in the problem-formulation process and fuzzy equal goals of the decision maker for each of the objective functions. The fuzzy parameters in the objective functions and the constraints characterized by fuzzy numbers. The proposed method can be applied to case with multiobjective problems and guarantee an efficient solution. An illustrative numerical example is presented.

• Journal of applied mathematics & informatics
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• 제7권1호
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• pp.183-193
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• 2000

In this paper we use measure theory to solve a wide range of second-order boundary value ordinary differential equations. First, we transform the problem to a first order system of ordinary differential equations(ODE's)and then define an optimization problem related to it. The new problem in modified into one consisting of the minimization of a linear functional over a set of Radon measures; the optimal measure is then approximated by a finite combination of atomic measures and the problem converted approximatly to a finite-dimensional linear programming problem. The solution to this problem is used to construct the approximate solution of the original problem. Finally we get the error functional E(we define in this paper) for the approximate solution of the ODE's problem.

• 한국경영과학회지
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• 제34권2호
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• pp.77-90
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• 2009

Total profit level Increases if a company increase the cost for achieving R&D related goals of equipment productivity enhancement, production cost saving, or for achieving equipment scale target, sales volume goal. But how much money should be invested to achieve a certain level of profit? We formulated the model to set the optimal goal levels to minimize the investment cost under the constraint that certain level of total profit should be guaranteed. This model derived from a case of P steel company. We found that this should be considered in relation with the production sales planning (known as optimal product mix problem) to guarantee the profit. We suggested a nonlinear programming model, 3 valiant form of the p+roduct mix problem. We can find the optimal Investment level for the R&D related goals or sales volume goal, equipment scale target for the P steel company using the model.

• 정보기술응용연구
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• 제3권3호
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• pp.25-40
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• 2001

본 논문에서는 유전적 프로그래밍(Genetic Programing)을 이용하여 최적의 다항식을 생성하는 기법을 제시하고자 한다. 다항식은 비선형성이 큰 응답면을 모델링해야 하며, 이를 위하여 GP 트리 생성시 2-3차 오더의 Taylor Series를 사용하는 방법을 시도하였다. 아울러 생서되는 다항식의 크기를 제어하기 위해서 GP 트리가 표현할 수 있는 다항식의 최대 차수를 제한함과 동시에 하나의 주 트리와 보 트리로 구성되는 GAGPT(Group of Additive Genetic Programming Trees) 사용을 모색하였다. 마지막으로 두 개의 응용 예를 통하여 본 방법의 유용성을 검증하였다.

• 품질경영학회지
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• 제5권1호
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• pp.23-29
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• 1977

Geometric programming is very useful for the solution of certain nonlinear programming problems in which the objective function and the constraints are posynomial expressions. By solving the dual program, it can be obtained that the solution of the primal program of Geometric programming. And, more efficient solution is to form an Augmented program possessing degree of difficult zero. A regional water-quality management problem may involve a multistage constrained optimization with many decision variables. In this problem, especially, appling that solution to it is also useful. This paper is described that : 1) the efficient solution of a water-quality management model formed by Geometric programming and 2) the algorithm developed to apply easily a real system by modifing and simplifing the solution.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Journal of Ship and Ocean Technology
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• 제11권4호
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• pp.21-28
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• 2007

Recently the importance of the utilization of engineering data is gradually increasing. Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper deals with generating optimal polynomials using genetic programming (GP) as the module of Data Mining system. Low order Taylor series are used to approximate the polynomial easily as a nonlinear function to fit the accumulated data. The overfitting problem is unavoidable because in real applications, the size of learning samples is minimal. This problem can be handled with the extended data set and function node stabilization method. The Data Mining system for the ship design based on polynomial genetic programming is presented.

• 한국국방경영분석학회지
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• 제18권1호
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• pp.47-60
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• 1992

A class of allocation problems can be modeled in a linear programming formulation. But in reality, the coefficient of both the cost and constraint equations can not be generally determined by crisp numbers due to the imprecision or fuzziness in the related parameters. To account for this. a fuzzy version is considered and solved by transforming to a conventional non-linear programming model. This gives a solution as well as the degree that the solution satisfies the objective and constraints simultaneously and hence will be very useful to a decision maker. An attacker assignment problem for multiple fired targets has been modeled by a linear programming formulation by Lemus and David. in which the objective is to minimize the cost that might occur on attacker's losses during the mission. A fuzzy version of the model is formulated and solved by transforming it to a conventional nonlinear programming formulation following the Tanaka's approach. It is also expected that the fuzzy approach will have wide applicability in general allocation problems

• 대한안전경영과학회지
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• 제15권1호
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• pp.77-85
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• 2013

In the location science, environmental effect becomes a new main consideration for site selection. For the unwanted facility location selection, decision makers should consider the cost of resolving the environmental conflict. We introduced the negative influence cost for the facility which was inversely proportional to distance between the facility and residents. An unwanted facility location problem was suggested to minimize the sum of the negative influence cost and the transportation cost. The objective cost function was analyzed as nonlinear type and was neither convex nor concave. Three GRASP (Greedy Randomized adaptive Search Procedure) methods as like Random_GRASP, Epsilon_GRASP and GRID_GRASP were developed to solve the unwanted facility location problem. The Newton's method for nonlinear optimization problem was used for local search in GRASP. Experimental results showed that quality of solution of the GRID_GRASP was better than those of Random_GRASP and Epsilon_GRASP. The calculation time of Random_GRASP and Epsilon_GRASP were faster than that of Grid_GRASP.