• 한국경영과학회지
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• 제28권2호
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• pp.129-138
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• 2003

The traveling salesman problem (TSP) is an NP-hard problem. As the number of nodes increases, it takes a lot of time to find an optimal solution. Instead of considering all arcs, if we select and consider only some arcs more likely to be included in an optimal solution, we can find efficiently an optimal solution. Arc candidate set is a group of some good arcs. For the Lack of study in the asymmetric TSP. it needs to research arc candidate set for the asymmetric TSP systematically. In this paper, we suggest a regression function determining arc candidate set for the asymmetric TSP. We established the function based on 2100 experiments, and we proved the goodness of fit for the model through various 787problems. The result showed that the optimal solutions obtained from our arc candidate set are equal to the ones of original problems. We expect that this function would be very useful to reduce the complexity of TSP.

• 한국항해항만학회지
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• 제34권1호
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• pp.59-64
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• 2010

본 연구는 마리나 건설 최적의 대상지를 결정하기 위하여 여러 가능해역 중에서 최적의 장소를 결정하는 것이다. 마리나 건설 후보지 선정 문제를 다루기 위해서 여수시 인근의 10개의 후보지를 대상으로 10개의 결정 요인들을 분석하여 최적의 장소를 선정한다. 본 논문에서는 가장 합리적인 장소를 결정하기 위하여 새로운 모델인 0-1정수 최적계획모형이 제안된다. 제안된 모형은 지금까지 최적 마리나 시설 분석 문제에 적용된 사례가 없다. 본 논문은 가장 합리적인 장소를 결정하는데 기여할 뿐만 아니라 제안된 모형은 타 지역의 마리나 시설 건설 후보지 선정 문제에도 적용할 수 있다.

• 한국지능시스템학회논문지
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• 제19권2호
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• pp.191-196
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• 2009

본 논문에서는 BPSO(Binary Particle Swarm Optimization)방법과 상호정보량을 이용한 속성선택기법을 제안한다. 제안된 방법은 상호정보량을 이용한 후보속성부분집합을 선택하는 단계와 BPSO를 이용한 최적의 속성부분집합을 선택하는 단계로 구성되어 있다. 후보속성부분집합 선택 단계에서는 독립적으로 속성들의 상호정보량을 평가하여 순위별로 설정된 수 만큼 후보속성들을 선택한다. 최적속성부분집합 선택 단계에서는 BPSO를 이용하여 후보속성부분집합에서 최적의 속성부분집합을 탐색한다. BPSO의 목적함수는 분류기의 정확도와 선택된 속성 수를 포함하는 다중목적함수(Multi-Object Function)을 이용하였다. 제안된 기법의 성능을 평가하기 위하여 유전자 데이터를 사용하였으며, 실험결과 기존의 방법들에 비해 우수한 성능을 보임을 알 수 있었다.

• 한국측량학회지
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• 제34권2호
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• pp.161-169
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• 2016

This paper is intended to propose a novel approach to select an optimal site for a small-scaled artificial recharge system installation using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with geospatial data. TOPSIS is a MCDM (Multi-Criteria Decision Making) method to choose the preferred one of derived alternatives by calculating the relative closeness to an ideal solution. For applying TOPSIS, in the first, the topographic shape representing optimal recovery efficiency is defined based on a hydraulic model experiment, and then an appropriate surface slope is determined for the security of a self-purification capability with DEM (Digital Elevation Model). In the second phase, the candidate areas are extracted from an alluvial map through a morphology operation, because local alluvium with a lengthy and narrow shape could be satisfied with a primary condition for the optimal site. Thirdly, a shape file over all candidate areas was generated and criteria and their values were assigned according to hydrogeologic attributes. Finally, TOPSIS algorithm was applied to a shape file to place the order preference of candidate sites.

• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.161-168
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• 2014

In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).

• 해양환경안전학회지
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• 제17권4호
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• pp.323-329
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• 2011

본 연구는 최대의 효과를 달성하기 위하여 정수 계획법을 이용하여 주어진 자연조건하에서 여러 대안 후보 입지 중 최적의 요트 계류장을 결정 하기위한 문제이다. 목포시 인근의 4개의 요트 계류장 후보지 중 최적의 후보지를 선정하기 위하여 21개의 요소들이 각각의 후보지들에 대하여 분석된다. 총 21개 요소들 중 개발 기간과 초기 투자비용은 다음 요소들에 비해서 1.5배의 가중치를 갖는다. 가중치 선형모델 분석 결과 네 곳의 후보지중 평화광장 수역이 가장 합리적인 장소로 선정되었다. 본 논문에서 소개된 모델은 지금까지 요트 계류장 선정 시 적용된 적이 없는 새로운 방법이다. 본 논문은 가장 합리적인 위치를 결정하는데 공헌할 뿐만 아니라 마리나 관련된 다른 분야에도 응용 적용가능하다.

• 대한전자공학회논문지TC
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• 제48권5호
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• pp.25-32
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• 2011

본 논문에서는 MIMO 시스템에서 최적의 수신 성능을 제공하면서 낮은 복잡도를 요구하는 부분 검색 공간 기반의 검파 기법이 제안된다. 최적의 최대 우도 검파기는 신호의 전체 검색 공간을 활용하기 때문에 많은 복잡도를 요구하게 되는 데, 이에 대한 해결 방안으로 신호의 부분 검색 공간에 기반한 부분 후보 심볼 벡터 생성 기법이 제안된다. 또한, 최적의 수신 성능을 제공하기 위해서 전체 검색 공간에 기반한 전체 후보 심볼 벡터 생성 기법이 제안된다. 결론적으로 제안하는 부분 검색 공간 기반의 검파 기법은 MIMO 시스템에서 낮은 복잡도를 필요하면서 최적의 수신 성능을 제공하는 검파기법으로 사용될 수 있다.

• 한국항해학회지
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• 제14권1호
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• pp.21-38
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• 1990

This paper discusses the various modes of operations of cargo ships which are liner operations, tramp shipping and industrial operations, and mathematical programming, simulation , and heuristic method that can be used to solve ships routing and scheduling problems for each of these operations. In particular, this paper put emphasis on a crude oil tanker scheduling problem. The problem is to achieve an optimal sequence of cargoes or an optimal schedule for each ship in a given fleet during a given period. Each cargo is characterized by its type, size, loading and discharging ports, loading and discharging dates, cost, and revenue. Our approach is to enumerate all feasible candidate schedate schedules for each ship, where a candidate schedule specifies a set of cargoes that can be feasibly carried by a ship within the planning horizon , together with loading and discharging dates for each cargo in the set. Provided that candidate schedules have been generated for each ship, the problem of choosing from these an optimal schedule for each ship is formulated as a set partitioning problem, a set packing problem, and a integer generalized network problem respectively. We write the PASCAL programs for schedule generator and apply our approach to the crude oil tanker scheduling problem similar to a realistic system.

• Structural Engineering and Mechanics
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• 제82권3호
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• 한국경영과학회지
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• 제29권3호
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• pp.145-155
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• 2004

The design of a communication network has long been a challenging optimization problem. Since the optimal design of a network topology is a well known as a NP-complete problem, many researches have been conducted to obtain near optimal solutions in polynomial time instead of exact optimal solutions. All of these researches suggested diverse heuristic algorithms that can be applied to network design problems. Among these algorithms, a simulated annealing algorithm has been proved to guarantee a good solution for many NP-complete problems. in applying the simulated annealing algorithms to network design problems, generating mechanisms for initial solutions and candidate solutions play an important role in terms of goodness of a solution and efficiency. This study aims at analyzing these mechanisms through experiments, and then suggesting reliable mechanisms.