• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.354-363
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• 2016

Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.783-783
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• 2012

본 연구에서는 낙동강 임하댐 유역을 대상으로 엔트로피 이론(혼합분포 적용)과 관측소의 공간적 분포를 동시에 고려하여 강우관측망을 평가하였다. 일반적으로 혼합분포를 이용하는 강우관측망 평가는 연속분포를 이용하는 경우 비해 강우의 시공간적 간헐성을 고려할 수 있다는 장점이 있다. 아울러 유역의 면적평균강우량을 산정시 강우관측소는 균등하게 설치된 경우가 가장 이상적이며, 이를 최근린 지수(Nearest neighbor index)를 이용하여 강우관측소 간에 공간적 분포를 등급화하였다. 최근린 지수는 임의의 점에 가장 가까운 인접 점들 간의 거리 특성을 이용하는 방법으로 점의 분포를 보다 지리적으로 파악할 수 있다. 본 연구에서는 엔트로피의 최대 정보전달량 및 강우관측소의 등급을 동시에 고려하기 위해 유클리디언 거리를 이용하여 2개의 목적함수를 통합하였으며, 이를 MOGA(Multi Objective Genetic Algorithm)를 이용하여 최적관측망을 선정하였다. 그 결과 MOGA를 이용하여 관측망을 평가한 경우 엔트로피 이론만을 적용했을 때보다 최적관측소가 보다 분산됨을 확인하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.157-165
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• 2011

Base isolation system is widely used for reduction of dynamic responses of structures subjected to seismic load. Recently, research on a smart base isolation system that can effectively reduce dynamic responses of the isolated structure without accompanying increases in base drifts has been actively conducted. In this study, a smart base isolation system was applied to an arch structure subjected to seismic excitation and its control performance for reduction of seismic responses was evaluated. In order to make a smart base isolation system, 4kN MR dampers and low damping elastomeric bearings were used. Seismic response control performance of the proposed smart base isolation system was compared to that of the optimally designed lead-rubber bearing(LRB) isolation system. To this end, an artificial ground motion developed based on KBC2009 design response spectrum was used as a seismic excitation. Fuzzy control algorithm was used to control MR damper in the smart base isolation system and multi-objective genetic algorithm was employed to optimize the fuzzy controller. Based on numerical simulation results, it has been shown that the smart base isolation system can drastically reduce base drifts and seismic responses of the example arch structure in comparison with LRB isolation system.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.539-550
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• 2005

The spanning tree, which is being used the most widely in indoor wiring network, is chosen for the network topology of the optimal LAN design. To apply a spanning tree to GA, the concept of $Pr\ddot{u}fer$ numbers is used. $Pr\ddot{u}fer$ numbers can express he spanning tree in an efficient and brief way, and also can properly represent the characteristics of spanning trees. This paper uses Pareto Stratum-Niche Cubicle(PS-NC) GA by complementing the defect of the same priority allowance in non-dominated solutions of pareto genetic algorithm(PGA). By applying the PS-NC GA to the LAN design areas, the optimal LAN topology design in terms of minimizing both message delay time and connection-cost could be accomplished in a relatively short time. Numerical analysis has been done for a hypothetical data set. The results show that the proposed algorithm could provide better or good solutions for the multi-objective LAN design problem in a fairly short time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.5
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• pp.535-541
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• 2015

In this study, an approximate multi-objective optimization of a wall-mounted monitor bracket arm was performed. The rotation angle of the bracket arm was determined considering the inplane degree of freedom. We then formulated an optimization problem on maximum stress and deflection. Analyses of mean and design parameters were conducted for sensitivity regarding performance with orthogonal array and response surface method (RSM). RSM models of objective and constraint functions were generated using central composite (CCD) and D-optimal design. The accuracy of approximate models was evaluated through $R^2$ value. The obtained optimal solutions by non-dominant sorting genetic algorithm (NSGA-II) were validated through the finite element analysis and we compared the obtained optimal solution by CCD and D-optimal design.

• Journal of Korean Society of Steel Construction
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• v.18 no.6
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• pp.677-686
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• 2006

In this paper, a more practical and realistic method is proposed to establish the lifetime optimum maintenance strategies of the deteriorating bridges considering the life-cycle performance as well as life-cycle cost. The genetic algorithm is applied to generate the set of maintenance scenarios that is the multi-objective combinatorial optimization problem related to lifetime performance and cost as separate objective functions, and the technique to select optimum tradeoff maintenance scenario is presented. Optimum maintenance scenarios could be generated not only at the individual member level but also at the system level of the bridge. Through the analytical results of applying the proposed methodology to the existing bridge, it is expected that the methodology will be effectively used to determine the optimum maintenance strategy for introducing a real preventive maintenance system and overcoming the limits of existing maintenance methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.402-410
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• 2013

In this paper, a novel and robust Power System Stabilizer (PSS) is proposed as an effective approach to improve stability in electric power systems. The dynamic performance of proposed PSS has been thoroughly compared with Conventional PSS (CPSS). Both the Real Coded Genetic Algorithm (RCGA) and Particle Swarm Optimization (PSO) techniques are applied to optimum tune the parameter of both the proposed PSS and CPSS in order to damp-out power system oscillations. Due to the high sufficiency of both the RCGA and PSO techniques to solve the very non-linear objective, they have been employed for solution of the optimization problem. In order to verify the dynamic performance of these devices, different conditions of disturbance are taken into account in Single Machine Infinite Bus (SMIB) power system. Moreover, to ensure the robustness of proposed PSS in damping the power system multi-mode oscillations, a Multi Machine (MM) power system under various disturbances are considered as a test system. The results of nonlinear simulation strongly suggest that the proposed PSS significantly enhances the power system dynamic stability in both of the SMIB and MM power system as compared to CPSS.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.118-125
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• 2003

This paper presents a scheme for the placement of dispersed generator systems(DGs) based on load model in unbalanced systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm The method proposed is applied to IEEE 13 bus unbalanced distribution systems to demonstrate its effectiveness.