• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.55-61
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• 2020

Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.15-22
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• 2023

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.1-8
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• 2024

The traveling salesman problem(TSP) is one of the well known combinatorial optimization problems. Local search has been used as a method to solve TSP. Greedy Random Insertion(GRI) is known as an effective neighbor generation method for local search. GRI selects some cities from the current solution randomly and inserts them one by one into the best position of the current partial solution considering only one city at a time. We first propose another greedy neighbor generation method which is named Full Greedy Insertion(FGI). FGI determines insertion location one by one like GRI, but considers all remaining cities at once. And then we propose a method to combine GRI with FGI, in which GRI or FGI is randomly selected and executed at each iteration in simulated annealing. According to the experimental results, FGI alone does not necessarily perform very well. However, we confirmed that the combined method outperforms the existing local search methods including GRI.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.9 no.13
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• pp.79-86
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• 1986

This paper presents a heuristic algorithm for a crew scheduling problem with dead head flights. This paper modifies and improves saving method for finding the Multiple Salesman tours in the graph. The results show that the computing time from this algorithm is implemented very much than those from general crew scheduling algorithms by set covering models.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.447-448
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• 2013

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.63-64
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• 2012

차분진화 알고리즘은 Storn 과 Price에 의해 제안된 메타휴리스틱 알고리즘이다. 본 논문에서는 외판원 문제를 해결하기 위한 차분진화 알고리즘을 소개한다. 차분진화 알고리즘은 실수 문제를 위한 알고리즘이므로 외판원 문제를 해결하기 위해 난수 키 표현법을 적용한다. OR Library의 표준 외판원 문제에 적용한 결과 제안한 알고리즘은 외판원 문제 해결에 가능성이 있음을 보여주었다.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.102-108
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• 2020

One of the key features of a mobile crowd-sensing (MCS) system is task allocation, which aims to recruit workers efficiently to carry out the tasks. Due to various constraints of the tasks (such as specific sensor requirement and a probabilistic guarantee of task completion) and workers heterogeneity, the task allocation become challenging. This assignment problem becomes more intractable because of the deadline of the tasks and a lot of possible task completion order or moving path of workers since a worker may perform multiple tasks and need to physically visit the tasks venues to complete the tasks. Therefore, in this paper, a hybrid search algorithm for task allocation called HST is proposed to address the problem, which employ a traveling salesman problem heuristic to find the task completion order. HST is developed based on the tabu search algorithm and exploits the premature convergence avoiding concepts from the genetic algorithm and simulated annealing. The experimental results verify that our proposed scheme outperforms the existing methods while satisfying given constraints.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.217-225
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• 1999

협상은 상거래에 있어서 매우 중요한 요소 중 하나이다. 현재의 웹 기반 전자상거래 시스템은 이러한 중요한 협상 구조를 상거래에 잘 반영하지 못하는 문제점을 가지고 있다. 이러한 문제점중 기업과 소비자간의 미비한 협상 구조를 보안하기 위해 실세계 상거래에서 존재하는 점원을 전자상거래상의 가상점원으로 모델링하여 회사의 정책과 구매자의 특성을 반영하여 구매자와 전략적으로 자동 협상을 수행할 수 있는 에이전트의 구조를 설계하고 구현하였다. 협상은 매우 복잡한 구조를 가지고 있다. 이러한 협상 구조를 지원하기 위해서는 상호간의 제안을 표현하고, 그 제안에 대한 평가 내용과 결정사항을 전달할 수 있는 언어적인 조가 필요하며, 협상의 대상이 되는 사안들의 특성을 반영할 수 있는 표현 구조도 요구된다. 또한 이러한 협상에서 전략을 세우고 알맞은 제안을 제시하며 상대의 제안에 대하여 전략적으로 반응할 수 있는 의사결정 모델이 요구된다. 본 논문에서는 회사의 정책 모델과 구매자의 모델을 정의하고 이를 이용한 협상 모델을 설계 구현하였다. 협상 구조의 모델링을 위해 KQML(Knowledge Query Manipulation Language)을 기반으로 전자상거래 프로토콜로 설계하고, 논쟁 기반 협상 모델을 기초로 협상언어를 설계하였다. 또한 협상에서의 전략적인 의사결정을 위해 게임이론을 이용하고, 규칙 기반 시스템으로 이를 보충하였다. 마지막으로 가상점원 모델을 바탕으로 조립 컴퓨터 판매를 위한 가상점원을 구현하였고, 이에 대한 실험을 통하여 가상점원의 유용성을 보였다.