• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.352-358
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• 2022

In this paper, we study the relationship between infection rates of covid 19 and the precautionary measures and strict protocols taken by Saudi Arabia to combat the spread of the coronavirus disease and minimize the number of infected people. Based on the infection rates and the timetable of precautionary measures, the best framework of precautionary measures was identified by applying the traveling salesman problem (TSP) that relies on ant colony optimization (ACO) algorithms. The proposed algorithm was applied to daily infected cases data in Saudi Arabia during three periods of precautionary measures: partial curfew, whole curfew, and gatherings penalties. The results showed the partial curfew and the whole curfew for some cities have the minimum total cases over other precautionary measures. The gatherings penalties had no real effect in reducing infected cases as the other two precautionary measures. Therefore, in future similar circumstances, we recommend first applying the partial curfew and the whole curfew for some cities, and not considering the gatherings penalties as an effective precautionary measure. We also recommend re-study the application of the grouping penalty, to identify the reasons behind the lack of its effectiveness in reducing the number of infected cases.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.61-63
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• 2003

지난 10년 동안 유전 알고리즘은 어렵고 복잡한 다양한 문제들을 해결하기 위한 새로운 방법으로 인식되어왔다. 이러한 유전 알고리즘의 성능은 알고리즘 내에 구현되는 여러 연산자들에 좌우된다. 따라서 많은 연구자들이 새로운 연산자 개발에 관심을 가져 왔었다. 특히, 가장 널리 알려진 조합최적화 문제 중에 하나인 알려진 traveling salesman problem (TSP)의 경우 NP-hard문제로 분류되어 현재까지 이를 해결하기 위한 다양한 유전 연산자들이 개발되어 왔었다. 따라서 본 논문에서는 TSP 문제를 test problem로 이용하여 이를 해결하기 위한 새로운 유전 연산자 특히 교차 (Crossover Operator) 연산자들을 제안하고 기존의 다양한 연산자들과 비교를 통해서 성능을 입증한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.181-188
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• 2007

Undirected Rural Postman Problem(URPP) is a problem that finds a shortest tour traversing the given arcs at least once in a given network. The URPP is one of the basic network problems used in solving the various real-world problems. And it is known as NP-Complete. URPP is an arc-oriented problem that the direction of a tour in an arc has to be considered. Hence, In URPP, it is difficult to use the algorithm for Traveling Salesman Problem (TSP), which is a node-oriented problem, directly. This paper proposes the decoding algorithm using graph transformation in the genetic algorithm for URPP. That is, you can find the entire tour traversing without considering the direction of arcs by transforming the arc-oriented graph into the node-oriented graph. This paper compares the performances of the proposed algorithm with an existing algorithm. In the simulation results, the proposed algorithm obtained better than the existing algorithm

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1279-1288
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• 2007

A vehicle routing problem with time constraint is one of the must important problem in distribution and logistics. In practice, the service for a customer must start and finish within a given delivery time. This study is concerned about the development of a model to optimize vehicle routing problem under the multi-logistics center problem. And we used a two-step approach with an improved genetic algorithm. In step one, a sector clustering model is developed by transfer the multi-logistics center problem to a single logistics center problem which is more easy to be solved. In step two, we developed a GA-TSP model with an improved genetic algorithm which can search a optimize vehicle routing with given time constraints. As a result, we developed a Network VRP computer programs according to the proposed solution VRP used ActiveX and distributed object technology.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.153-168
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• 2012

It has been recognized as an important issue to design optimally a firm's logistics network for minimizing logistics cost and maximizing customer service. It is, however, not easy to get an optimal solution by analyzing trade-off of cost factors, dynamic and interdependent characteristics in the logistics network decision making. Although there has been some developments in a system which helps decision making for logistics analysis, it is true that there is no system for enterprise-wise's on-site support and methodical logistics decision. Specially, E-biz process along with information technology has been made dramatic advance in a various industries, there has been much need for practical education closely resembles on-site work. The software developed by this study materializes efficient algorithm suggested by recent studies in key topics of logistics such as location and allocation problem, traveling salesman problem, and vehicle routing problem and transportation and distribution problem. It also supports executing a variety of experimental design and analysis in a way of the most user friendly based on Java. In the near future, we expect that it can be extended to integrated supply chain solution by adding decision making in production in addition to a decision in logistics.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.135-138
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• 1996

In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.5-169
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• 2001

This study proposes a neural network for solving optimization problems such as the TSP (Travelling Salesman Problem), scheduling, and line balancing. The Hopfield network has been used for solving such problems, but it frequently gives abnormal solutions or non-optimal ones. Moreover, the Hopfield network takes much time especially in solving large size problems. To overcome such disadvantages, this study adopts nodes whose outputs changes with a fixed value at every evolution. The proposed network is applied to solving a TSP, finding the shortest path for visiting all the cities, each of which is visted only once. Here, the travelling path is reflected to the energy function of the network. The proposed network evolves to globally minimize the energy function, and a ...

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.601-605
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• 1991

In this paper an approximate solution method for the large-scale Traveling Salesman Problem(TSP) is presented. The method start with the subdivision of the problem domain into a number of clusters by considering their geometries. The clusters have limited number of nodes so as to get local solutions. They are linked to give the least path which covers the whole domain and become TSPs with start- and end-node. The approximate local solutions in each cluster are obtained by using geometrical property of the cluster, and combined to give an overall-approximate solution for the large-scale TSP.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.317-320
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• 2003

본 논문에서는 각 도시마다 가중치가 있는 City domain을 tour하기 위한 문제를 해결하기 위해 Simulated Annealing Algorithm을 확장한 알고리즘을 제시하였고 Capacitated vehicle routing problem을 변형한 Augmented multiple salesman traveling problem을 정의하고 이를 해결하기 위한 에너지 함수와 알고리즘을 제시하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.64-66
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• 2003

DNA(Deoxyribo Nucleic Acid)컴퓨팅은 생체분자를 계산의 도구로 이용하는 새로운 계산 방법으로 DNA 정보 저장능력과 DNA의 상보적인 관계를 이용하여 연산을 수행하는 방법이다. 최근에는 DNA 분자들이 갖는 강력한 병렬성을 이용하여 NP-Complete 문제에 적용하는 연구가 많이 시도되고 있다. Adleman이 DNA 컴퓨팅을 이용해 해결한 HPP(Hamilton Path Problem)와는 달리 TSP(Traveling Salesman Problem)는 간선에 가중치가 추가되었기 때문에 DNA 염기배열로 표현하기가 어렵고 또한 염기배열의 길이를 줄이기 위해 고정길이 염기배열을 사용할 경우 가중치가 커지면 효율적이지 못하다. 본 논문에서는 스무딩 알고리즘(smoothing algorithm)을 사용하여 간선의 가중치를 일정한 비율로 줄인 다음 유전자 알고리즘을 사용하여 최적의 염기배열을 찾는 방법을 제안하였다.