• 대한안전경영과학회지
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• 제14권1호
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• pp.167-177
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• 2012

In this paper, we raised the performance of heuristic algorithm to assign job to workers in parallel line inspection process without sequence. In previous research, we developed the heuristic algorithm. But the heuristic algorithm can't find optimal solution perfectly. In order to solve this problem, we proposed new method to make initial solution called FN(First Next) method and combined the new FN method and old FE method using previous heuristic algorithm. Experiments of assigning job are performed to evaluate performance of this FE+FN heuristic algorithm. The result shows that the FE+FN heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are optimal solutions, this heuristic algorithm can find the optimal solution perfectly.

• 한국컴퓨터정보학회논문지
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• 제20권8호
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• pp.105-112
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• 2015

Generally, the optimal solution of assignment problem has been obtained by Hungarian algorithm with O($n^3$) time complexity. This paper proposes more simple algorithm with O($n^2$) time complexity than Hungarian algorithm. The proposed algorithm simply selects minimum cost in each row, and classified into set S, H, and T. Then, the minimum cost is moved from S to T and $S{\rightarrow}H$, $H{\rightarrow}T$. The proposed algorithm can be obtain the same optimal solution as well-known algorithms and improve the optimal solution of partial unbalanced assignment problems.

• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.183-194
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• 2003

We will explain a new method for obtaining the nearly optimal domain for optimal shape design problems associated with the solution of a nonlinear wave equation. Taking into account the boundary and terminal conditions of the system, a new approach is applied to determine the optimal domain and its related optimal control function with respect to the integral performance criteria, by use of positive Radon measures. The approach, say shape-measure, consists of two steps; first for a fixed domain, the optimal control will be identified by the use of measures. This function and the optimal value of the objective function depend on the geometrical variables of the domain. In the second step, based on the results of the previous one and by applying some convenient optimization techniques, the optimal domain and its related optimal control function will be identified at the same time. The existence of the optimal solution is considered and a numerical example is also given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.440-444
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• 1998

In this paper, we consider numerical solution of a H-J-B (Hamilton-Jacobi-Bellman) equation of elliptic type arising from the stochastic control problem. For the numerical solution of the equation, we take an approach involving contraction mapping and finite difference approximation. We choose the It(equation omitted) type stochastic differential equation as the dynamic system concerned. The numerical method of solution is validated computationally by using the constructed test case. Map of optimal controls is obtained through the numerical solution process of the equation. We also show how the method applies by taking a simple example of nonlinear spacecraft control.

• Management Science and Financial Engineering
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• 제10권2호
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• pp.103-118
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• 2004

$\epsilon$-sensitivity analysis is a kind of methods for performing sensitivity analysis for linear programming. Its main advantage is that it can be directly applied for interior-point methods with a little computation. Although $\epsilon$-sensitivity analysis was proposed several years ago, there have been no studies on its relationship with other sensitivity analysis methods. In this paper, we discuss the relationship between $\epsilon$-sensitivity analysis and sensitivity analysis using an optimal basis. First. we present a property of $\epsilon$-sensitivity analysis, from which we derive a simplified formula for finding the characteristic region of $\epsilon$-sensitivity analysis. Next, using the simplified formula, we examine the relationship between $\epsilon$-sensitivity analysis and sensitivity analysis using optimal basis when an $\epsilon$-optimal solution is sufficiently close to an optimal extreme solution. We show that under primal nondegeneracy or dual non degeneracy of an optimal extreme solution, the characteristic region of $\epsilon$-sensitivity analysis converges to that of sensitivity analysis using an optimal basis. However, for the case of both primal and dual degeneracy, we present an example in which the characteristic region of $\epsilon$-sensitivity analysis is different from that of sensitivity analysis using an optimal basis.

• 한국인터넷방송통신학회논문지
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• 제13권2호
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• pp.93-102
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• 2013

본 논문은 수송 문제의 최적 해를 찾는 방법을 제안하였다. 수송 문제는 공급량과 요구량이 동일한 균형 수송과 공급량과 요구량이 다른 불균형 문제로 구분된다. 수송문제의 최적 해를 얻는 대표적인 TSM은 먼저, 불균형 수송 문제인 경우 가상의 행이나 열을 추가하여 균형 수송 문제로 변환시킨다. 다음으로 NCM, LCM, VAM 등 다양한 방법을 적용하여 초기 해를 구한다. 마지막으로 초기 해가 최적 해인지 검증하는 MODI를 적용한다. 따라서 최적 해를 구하는 과정이 복잡하다. 제안된 방법은 불균형을 균형 수송 문제로 변환하는 과정을 거치지 않고 직접 적용한다. 또한, 초기 해가 최적해인지 검증하는 과정도 수행하지 않는다. 제안된 방법은 첫 번째로, 행에 대해 공급량을 비용 오름차순으로 요구량을 만족하도록 배정한다. 두 번째로, 각 열에 대해 배정된 량이 요구량을 초과하는 순으로 배정량을 조정한다. 배정량 조정 방법은 다음 수행 순위 열의 비용과의 차이인 손실비용이 가장 큰 셀에 우선 배정하고 나머지 셀에 대해서는 배정량을 조정한다. 조정된 배정량은 요구량을 만족하지 못하는 수행 순위 오름차순 셀들에 추가된다. 모든 열에 대해 배정량이 조정되면 마지막으로 행의 최소 비용에 미 배정되었거나 열의 최대 비용에 배정된 경우 배정량을 상호 교환하는 방법으로 추가 조정한다. 불균형 배송 2개와 균형 배송 13개 데이터에 제안된 방법을 적용한 결과 모두 최적 해를 구하는데 성공하였다. 또한, 기존의 방법들이 최적해를 구하지 못한 4개 데이터에 대해서 추가로 최적 해를 구하였다. 따라서 제안된 방법은 수송 문제에 대해 일반화된 단일 방법으로 적용할 수 있을 것이다.

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.175-184
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• 2009

In this paper, a method for optimal measurement placement in the problem of static harmonic state estimation in power systems is proposed. At first, for achieving to a suitable method by considering the precision factor of the estimation, a procedure based on Genetic Algorithm (GA) for optimal placement is suggested. Optimal placement by regarding the precision factor has an evident solution, and the proposed method is successful in achieving the mentioned solution. But, the previous applied method, which is called the Sequential Elimination (SE) algorithm, can not achieve to the evident solution of the mentioned problem. Finally, considering both precision and economic factors together in solving the optimal placement problem, a practical method based on GA is proposed. The simulation results are shown an improvement in the precision of the estimation by using the proposed method.

• 한국컴퓨터정보학회논문지
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• 제20권12호
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• pp.75-82
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• 2015

Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm of two-sided optimization with time complexity $O(n^4)$. This paper suggests one-sided optimal assignment and swap optimization algorithm with time complexity $O(n^2)$ can be achieve the goal of two-sided optimization. This algorithm selects the minimum cost for each row, and reassigns over-assigned to under-assigned cell. Next, that verifies the existence of swap optimization candidates, and swap optimizes with ${\kappa}-opt({\kappa}=2,3)$. For 27 experimental data, the swap-optimization performs only 22% of data, and 78% of data can be get the two-sided optimal result through one-sided optimal result. Also, that can be improves on the solution of best known solution for partial problems.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• 전기의세계
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• 제22권1호
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• pp.28-34
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• 1973

This paper treats with the sub-optimal control problem of noninear systems by approximation method. This method involves the approximation by linearization which provides the sub-optimal solution of non-linear control problems. The result of this work shows that, in the problem in which the controlled plant is characterized by an ordinary differential equation of first order, the solution obtained by this method coincides with the exact solution of problem. In of case of the second or higher order systems, it is proved analytically that this method of linearization produces the sub-optimal solution of the given problem. It is also shown that the sub-optimality of solution by the method can be evaluated by introducing the upper and lower bounded performance indices. Discussion is made on the procedure with some illustrative examples whose performance indices are given in the quadratic forms.