• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.278-278
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve optimization problems such as the Traveling Salesman Problem(TSP) and maximizing or minimizing functions. In particular, because TSP is a well-known combinational optimization problem andbelongs to a NP-complete problem, there is huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. Additionally, the non-schema part is applied to inversion for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.171-178
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• 2012

A possible heuristic to solve metropolitan area vehicle routing problems with variable vehicle speeds is suggested in this research. Delivery hours are classified into 4 different time zones to make variable vehicle speeds no change within the same time zone to make TDVRP simple to solve. The suggested heuristic consists of 2 stages such as initial solution development step and initial solution improvement step. A computer program using C++ is constructed to evaluate the suggested heuristic. Randomly generated vehicle routing problems are used for the experiments. This heuristic could be helpful to logistics companies by increasing delivery efficiencies since the 4 zone classification is taken from the observed traffic information offered by a local government.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.103-107
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• 2010

게임이나 네비게이션 시스템, 관광경로 설계에 있어서 경로찾기는 매우 중요한 부분 중의 하나이다. 일반적으로 TSP(Traveling Salesman Problem), RPP(Rural Postman Problem), CPP(Chinese Postman Problem)와 같은 경로찾기 문제들은 일반적인 알고리즘으로 최적해를 구할 수 없다. 문제크기가 커질수록 해집합이 폭발적으로 커짐으로써 전체 해집합을 탐색하는데 많은 비용이 든다. 따라서, 이러한 문제들은 유전알고리즘이나 Simulated Annealing과 같은 휴리스틱 알고리즘을 이용하여 근사최적 경로를 찾는다. 본 논문에서는 이와 같은 경로찾기 문제의 근사 최적해를 구하기 위한 시뮬레이션 시스템을 설계하고 구현하였다. 본 연구에서 구현한 시뮬레이션 시스템에는 유전알고리즘 엔진(GA 엔진)과 사용자 인터페이스를 제공한다. 사용자 인터페이스는 유전알고리즘에 사용될 파라미터를 설정하는 부분이며, GA 엔진은 유전알고리즘의 연산자들을 제공하는 부분이다. 본 논문에서 구현한 시뮬레이션 시스템은 게임과 같은 경로찾기 등에 활용될 수 있다.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.47-61
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• 2007

This paper presents two elimination methods of subtours, which is obtained by applying the Out-of-Kilter algorithm to the sequential ordering problem (SOP) to produce a feasible solution for the SOP. Since the SOP is a kind of asymmetric traveling salesman problem (ATSP) with precedence constraints, we can apply the Out-of-Kilter algorithm to the SOP by relaxing the precedence constraints. Instead of patching subtours, both of two elimination methods construct a feasible solution of the SOP by using arcs constructing the subtours, and they improve solution by running 3-opt and 4-opt at each iteration. We also use a perturbation method. cost relaxation to explore a global solution. Six cases from two elimination methods are presented and their experimental results are compared to each other. The proposed algorithm found 32 best known solutions out of the 34 instances from the TSPLIB in a reasonable time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.232-235
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve the Traveling Salesman Problem(TSP). Because TSP is a well-known combinatorial optimization problem and belongs to a NP-complete problem, there is a huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. In addition, we have the non-schema part to be applied to inversion as well as for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• IE interfaces
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• v.13 no.2
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• pp.195-203
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• 2000

This paper considers the problem of sequencing printed circuit boards(PCBs) on an automatic surface mount device(SMD) placement machine in order to minimize total setup time. Since the total set of component feeders needed by all boards cannot be loaded simultaneously on the magazine, the setup must be made between two successive boards in the sequence. It is assumed that the setup time depends on the number of component feeders to be replaced in the magazine. An important characteristic is that each feeder occupies a different number of slots in the magazine. This problem is equivalent to travelling salesman problem(TSP) except that the distances between two cities, that is, the setup times between two boards, are not known in advance. So, TSP-based heuristics with new distance functions are presented and their performances are compared through various test problems. Computational results indicate that our heuristics outperform existing methods.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.1-7
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• 2015

This research is to develop a possible process to apply k-means clustering to an efficient vehicle routing process under time varying vehicle moving speeds. Time varying vehicle moving speeds are easy to find in metropolitan area. There is a big difference between the moving time requirements of two specific delivery points. Less delivery times are necessary if a delivery vehicle moves after or before rush hours. Various vehicle moving speeds make the efficient vehicle route search process extremely difficult to find even for near optimum routes due to the changes of required time between delivery points. Delivery area division is designed to simplify this complicated VRPs due to time various vehicle speeds. Certain divided area can be grouped into few adjacent divisions to assume that no vehicle speed change in each division. The vehicle speeds moving between two delivery points within this adjacent division can be assumed to be same. This indicates that it is possible to search optimum routes based upon the distance between two points as regular traveling salesman problems. This makes the complicated search process simple to attack since few local optimum routes can be found and then connects them to make a complete route. A possible method to divide area using k-means clustering is suggested and detailed examples are given with explanations in this paper. It is clear that the results obtained using the suggested process are more reasonable than other methods. The suggested area division process can be used to generate better area division promising improved vehicle route generations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2343-2348
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• 2008

Ant algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the improved $AS_{rank}$ algorithms. The original $AS_{rank}$ algorithm accomplishes a pheromone updating about only the paths which will be composed of the optimal path is higher, but, the paths which will be composed the optimal path is lower does not considered. In this paper, The proposed method evaporate the pheromone of the paths which will be composed of the optimal path is lowest(worst tour path), it is reducing the probability of the edges selection during next search cycle. Simulation results of proposed method show lower average search time and average iteration than original ACS.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.27-35
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• 2021

Local search has been used to solve various combinatorial optimization problems. One of the most important factors in local search is the method of generating a neighbor solution. In this paper, we propose neighbor generation strategies of local search for permutation-based combinatorial optimization, and compare the performance of each strategies targeting the traveling salesman problem. In this paper, we propose a total of 10 neighbor generation strategies. Basically, we propose 4 new strategies such as Rotation in addition to the 4 strategies such as Swap which have been widely used in the past. In addition, there are Combined1 and Combined2, which are made by combining basic neighbor generation strategies. The experiment was performed by applying the basic local search, but changing only the neighbor generation strategy. As a result of the experiment, it was confirmed that the performance difference is large according to the neighbor generation strategy, and also confirmed that the performance of Combined2 is the best. In addition, it was confirmed that Combined2 shows better performance than the existing local search methods.