• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.83-88
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• 2002

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network (FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the experts' conventional methodology which is similar to the golden section search algorithm.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.261-263
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• 2008

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.182-191
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• 2011

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2338-2353
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• 2015

In this paper, the optimal power allocation algorithm that minimizes the aggregate bit error rate (BER) of the secondary user (SU) in a downlink orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) system, while subjecting to the interference power constraint and the transmit power constraint, is investigated under the assumption that the instantaneous channel state information (CSI) of the interference links between the secondary transmitter and the primary receiver, and between the primary transmitter and the secondary receiver is perfectly known. Besides, a suboptimal algorithm with less complexity is also proposed. In order to deal with more practical situations, we further assume that only the channel distribution information (CDI) of the interference links is available and propose heuristic power allocation algorithms based on bisection search method to minimize the aggregate BER under the interference outage constraint and the transmit power constraint. Simulation results are presented to verify the effectiveness of the proposed algorithms.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.17-28
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• 1998

This study suggests a procedure for designing cellular manufacturing systems (CMS) which are combined with automated guided vehicles (AGVs) using a tandem configuration. So far most of the previous studies have dealt with conventional design problems not considering the layout and the characteristics of transporters used in CMS. A mathematical model is developed using the service time to perform material transfers as a suitable meassure. The service capacity of AGVs and space limitations are also reflected in this model. As the model can be shown strongly NP-hard, a heuristic algorithm is presented, in which each cell is temporarily formed using both the set covering model and similarity coefficients, and then locations of the cells are determined by means of tabu search and finally machine perturbations are carried out. An example problem is solved to demonstrate the algorithm developed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.179-186
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• 2006

There was a lot of research to integration of the transshipment and inventory problem in supply chain. Such a integration of inventory and transshipment problem called IRP (Inventory Routing Problem). We consider a distribution problem in which a set of products has to be shipped from a supplier to several retailers in a given planning horizon. Transshipment from the supplier to the retailer is performed by vehicles of limited capacity. Each retailer determines replenishment leadtime and order quantity with buffer management. A supplier determines optimal vehicle routing in supply chain. We suggest a heuristic algorithm which be used TOC buffer management in a replenishment problem and a tabu search algorithm in VRP (Vehicle Routing Problem).

• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.72-76
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• 2004

CAD (Computer-Aided Design) 시스템은 보다 정확한 공학적 분석과 보다 많은 설계 대안을 고려하므로 설계의 질을 증가시킨다. 이러한 과정 중에 수행되는 최적화에는 다양한 탐색 기법들이 사용되고 있다. 그레이팅 설계를 자동화하는 AutoCAD 기반 시스템인 GDS(Grating automatic Drawing System)에서도 다양한 탐색 기법들이 최적화를 위해 사용되고 있다. 실제 그레이팅의 설계 공정에 있어서, 그레이팅은 특정 제약 조건과 우선순위에 맞추어 배치되어야한다. 그러나 현 GDS는 이러한 조건을 만족하는 최적 해를 찾는 것을 보장하지 않는다. 그러므로 본 논문에서는 그레이팅 배치에서 준수해야할 제약 조건과 우선순위를 만족하는 최적 해를 찾기 위한 하용 가능한 $A^{*}$ 알고리즘을 GDS에 적용하며, 시뮬레이션을 통하여 균등 비용 탐색과 상대적인 효율성을 평가한다.

• Korean Management Science Review
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• v.25 no.3
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• pp.87-99
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• 2008

Besieged by needs for upgrading the current Internet, social pressures, and regulatory concerns, a network operator may be left with few options to Improve his services. Yet he can still consider a transition prioritizing network services. In this paper, we describe a transition from a non-priority system to a prioritized one, using non-preemptive M/G/1 model. After reviewing the constraints and theoretical results from past research, we describe steps making the transition Pareto-improving, which boils down to a multi-goal search for a Pareto-improving state. We use a genetic algorithm that captures actual transition costs along with incentive-compatible and Pareto-Improving constraints. Simulation results demonstrate that the initial post-transition solutions are typically Pareto-improving. for non Pareto-improving solutions, the heuristic quickly generates Pareto-improving and incentive-compatible solutions.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.201-219
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• 2014

This paper considers service deployment and priority optimization for multiple service-oriented applications sharing reusable services, which are deployed as multiple instances in the cloud. In order to handle variations in the workloads of the multiple applications, service instances of the individual reusable services are dynamically provisioned in the cloud. Also service priorities for each application in a particular reusable service are dynamically adjusted. In this paper, we propose an analytic performance model, based on a queueing network model, to predict the expected sojourn times of multiple service-oriented applications, given the number of service instances and priority disciplines in individual reusable services. We also propose a simple heuristic algorithm to search an optimal number of service instances in the cloud and service priority disciplines for each application in individual reusable services. A numerical example is also presented to demonstrate the applicability of the proposed performance model and algorithm to the proposed optimal decision problem.

• Proceedings of the IEEK Conference
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• summer
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• pp.400-404
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• 2004

There are many methods to find solutions for Integer Programming problems (IPs) such as the Branch-Bound philosophy or the Cutting Plane algorithm. However, most of them have a problem that is the explosion of sets in the computing process. In addition, GA is known as a heuristic search algorithm for solutions of optimization problems. It is started from a random initial guess solution and attempting to find one that is the best under some criteria and conditions. The paper will study an artificial intelligent method to solve IPs by using Genetic Algorithms (GAs). The original solution of this was presented in the papers of Fabricio Olivetti de Francaand and Kimmo Nieminen [2003]. However, both have several limitations which causes could be operations in GAs. The paper proposes a method to upgrade these operations and computational results are also shown to support these upgrades.