• 품질경영학회지
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• 제24권4호
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• pp.44-58
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• 1996

This paper presents optimal accelerated life test plans with different censoring times for exponential, Weibull, and lognormal lifetime distributions, respectively. For an optimal plan, low stress level, proportion of test units allocated and censoring time at each stress are determined such that the asymptotic variance of the maximum likelihood estimator of a certain quantile at use condition is minimized. The proposed plans are compared with the corresponding optimal plans with a common censoring time over range of parameter values. Computational results indicate that those plans are statistically optimal ones in terms of accuracy of estimator when total censoring times of two plans are equal.

• 한국경영과학회지
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• 제26권4호
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• pp.83-97
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• 2001

Net Present Value (NPV) criterion has been the most widely used criterion to evaluate investment opportunities. However, the analysis based on the NPV criterion falls to consider the managerial flexibility of deferring decisions until major uncertainty is resolved. Recently, real options method attracted a lot of attention as a Powerful approach to address the problem. If investment decision is deferred, the value of the investment opportunity increases but opportunity cost increases at the same time. Therefore, it is important to decide the optimal timing how long the decision can be deferred. In this paper, we developed a model deciding the optimal decision timing. Using the real options approach, the model derived the optimal deferring time until a decision is made. Then, the model was applied to a Korean mobile telecommunications company who wants to invest on the wireless resale business. We believe that this model would be very useful to overcome the problem of NPV decision criterion. With this approach, we can make contingent decisions based on the observation of uncertainly resolutions.

• Structural Engineering and Mechanics
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• 제23권4호
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• 한국군사과학기술학회지
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• 제1권1호
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• pp.211-218
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• 1998

As a part of trajectory modulation to increase system survivability and terminal effectiveness, impact angle control is required in the terminal phase of tactical missile systems. The missile systems are not allowed to have high altitude to reduce probability of detection by sensors of missile defense systems. In this paper, an analytic form of a time-optimal control law is suggested in the case of constrained missile maneuverability and impact angle under the assumption of a zero-lag autopilot. The control law is obtained by establishing optimal missile-target engagement geometry in the vertical plane. Extension of the law for missiles with autopilot response lags requiring a numerical solution is studied by introducing an iterative algorithm for optimal switching time determination of which the initial switching instants are obtained from the analytic solution. Also suggested is a closed-form impact angle control law derived by an energy-optimal approach. The performances of the proposed guidance laws are evaluated by a series of computer runs.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.750-755
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• 2014

Product shortage which causes backordering and/or lost sales cost is very popular in chemical industries, especially in commodity polymer business. This study deals with backordering cost in the supply chain optimization model under the framework of process-inventory network. Classical economic order quantity model with backordering cost suggested optimal time delay and lot size of the final product delivery. Backordering can be compensated by advancing production/transportation of it or purchasing substitute product from third party as well as product delivery delay in supply chain network. Optimal solutions considering all means to recover shortage are more complicated than the classical one. We found three different solutions depending on parametric range and variable bounds. Optimal capacity of production/transportation processes associated with the product in backordering can be different from that when the product is not in backordering. The product shipping cycle time computed in this study was smaller than that optimized by the classical EOQ model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.432-435
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• 1995

In this paper a new approach to obtain the solution of the linear-quadratic Gaussian control problem for singularly perturbed discrete-time stochastic systems is proposed. The alogorithm proposed is based on exploring the previous results that the exact solution of the global discrete algebraic Riccati equations is found in terms of the reduced-order pure-slow and pure-fast nonsymmetric continuous-time algebraic Riccati equations and, in addition, the optimal global Kalman filter is decomposed into pure-slow and pure-fast local optimal filters both driven by the system measurements and the system optimal control input. It is shown that the optimal linear-quadratic Gaussian control problem for singularly perturbed linear discrete systems takes the complete decomposition and parallelism between pure-slow and pure-fast filters and controllers.

• 대한전기학회논문지:전력기술부문A
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• 제48권7호
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• pp.815-823
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• 1999

This study considers an implementation of artificial neural networks to the receding horizon optimal control and is applications to power systems. The Generalized Backpropagation-Through-Time (GBTT) algorithm is presented to deal with a quadratic cost function defined in a finite-time horizon. A decentralized approach is used to control the complex global system with simpler local controllers that need only local information. A Neural network based Receding horizon Optimal Control (NROC) 1aw is derived for the local nonlinear systems. The proposed NROC scheme is implemented with two artificial neural networks, Identification Neural Network (IDNN) and Optimal Control Neural Network (OCNN). The proposed NROC is applied to a power system to improve the damping of the low-frequency oscillation. The simulation results show that the NROC based power system stabilizer performs well with good damping for different loading conditions and fault types.

• 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.

• 전자공학회논문지C
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• 제34C권12호
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• pp.43-53
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• 1997

In this paper, the optimal synthesis algorithm of multiple-valued logic circuits using universal logic modules (ULM) U$_{f}$ based on 3-variable ternary reed-muller expansions is presented. We check the degree of each varable for the coefficients of reed-muller expansions and determine the order of optimal control input variables that minimize the number of ULM U$_{f}$ modules. The order of optimal control input variables is utilized the realization of multiple-valued logic circuits to be constructed by ULM U$_{f}$ modules based on reed-muller expansions using the circuit cost matrix. This algorithm is performed only unit time in order to search for the optimal control input variables. Also, this algorithm is able to be programmed by computer and the run time on programming is O(p$^{n}$ ).

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.471-477
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• 2014

The novel permanent magnetic actuator (PMA) and its optimal design method were proposed in this paper. The proposed PMA is referred to as the separated permanent magnetic actuator (SPMA) and significantly superior in terms of its cost and performance level over a conventional PMA. The proposed optimal design method uses the evolutionary strategy algorithm (ESA), the kriging meta-model (KMM), and the multi-step optimization. The KMM can compensate the slow convergence of the ESA. The proposed multi-step optimization process, which separates the independent variables, can decrease time and increase the reliability for the optimal design result. Briefly, the optimization time and the poor reliability of the optimum are mitigated by the proposed optimization method.