• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.169-178
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• 1996

Geographically dispersed computing system is made of computers interconnected by a telecommunications network. To make the system operated efficiently, system designer must determine the allocation of data files to each node. In designing such distributed computing system, the most important issue is the determination of the numbers and the locations where database files are allocated. This is commonly referred to as the file allocation problem (FAP)[3]. The proposed model is a 0/l integer programming problem minimizing the sum of file storage costs and communication(query and update) costs. File allocation problem belongs to the class of NP-Complete problems. Because of the complexity, it is hard to solve. So, this paper presents an efficient heuristic algorithm to solve the file allocation problem using Tabu Search Technique. By comparing the optimal solutions with the heuristic solutions, it is believed that the proposed heuristic algorithm gives good solutions. Through the experimentation of various starting points and tabu restrictions, this paper presents fast and efficient method to solve the file allocation problem in the distributed computing system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.99-101
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• 2000

DNA 컴퓨팅 기법은 실제 생체 분자(bio-molecule)를 계산의 도구로 사용하는 새로운 계산 방법으로, 진화 연산과 결합하여 인공지능의 새로운 분야로 부각되고 있다. 그러나, 실제 생체 분자를 계산의 도구로 사용하기 때문에 기존의 컴퓨터에 적용하기 어렵고, 단순히 합성과 분리라는 간단한 방법으로 해를 구하기 때문에 보다 효과적인 알고리즘을 개발하여야 할 필요성이 있다. 따라서, 본 논문에서는 DNA 컴퓨팅 기법을 컴퓨터에 적용하기 위한 방법으로 DNA 컴퓨팅에서의 코드 합성 기법과 유전자 알고리즘을 이용하여 NP-complete 문제중의 하나인 Sub-Set Sum 문제를 해결하여 그 결과를 분석한다. Sub-Set Sum 문제에서 단순 유전자 알고리듬보다 DNA 코드 유전자 알고리즘이 높은 성능을 보인다.

• Korean Management Science Review
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• v.28 no.2
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• pp.53-62
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• 2011

The objective of this paper is to propose a Binary Particle Swarm Optimization(BPSO) for design of reporting cell management system. The assignment of cells to reporting or non-reporting cells is an NP-complete problem having an exponential complexity in the Reporting Cell Location Management(RCLM) system. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration(location update) and search(paging) operations to minimize the cost of RCLM system. Experimental results demonstrate that BPSO is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.45-57
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• 2010

This paper considers a 2-stage assembly flowshop scheduling problem where each job is completed by assembling multiple components. The problem has the objective measure of minimizing total completion time. The problem is shown to be NP-complete in the strong sense. Thus, we derive some solution properties and propose three heuristic algorithms. Also, a mixed-integer programming model is developed and used to generate a lower bound for evaluating the performance of proposed heuristics. Numerical experiments demonstrate that the proposed heuristics are superior over those of previous research.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.166-169
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• 1997

For parallel processing, the compiler partitions a loaded program into a set of tasks and makes a schedule for the tasks that will minimize parallel processing time for the loaded program. Building an optimal schedule for a given set of partitioned tasks of a program has known to be NP-complete. In this paper we introduce a GA(Genetic Algorithm)-based scheduling method in which a chromosome consists of two parts of a string which decide the number and order of tasks on each processor. An additional computation is used for feasibility constraint in the chromosome. By granularity theory, a partitioned program is categorized into coarse-grain or fine-grain types. There exist good heuristic algorithms for coarse-grain type partitioning. We suggested another GA adaptive to the coarse-grain type partitioning. The infeasibility of chromosome is overcome by the encoding and operators. The number of processors are decided while the GA find the minimum parallel processing time.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.8-14
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• 2012

Two dimensional nesting is a common problem in industries such as the shipbuilding, automotive, clothing, shoe-making, and furniture industries, in which various parts are cut off from stock or packed in a flat space while minimizing waste or unoccupied space. Nesting is known as an NP-complete problem, which has a solution time proportional to the superpolynomial of the input size. It becomes practically impossible to find an optimal solution using algorithmic methods as the number of shapes to nest increases. Therefore, heuristic methods are commonly used to solve nesting problems. This paper presents an expert system that uses a heuristic search method based on an evaluation function for nesting problems, in which parts and stock are represented by pixels. The system is developed in CLIPS, an expert system shell, and is applied to four different kinds of example problems to verify its applicability in practical problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.103-114
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• 2008

Overlay Multicast is a promising approach to overcome the implementation problem of IP multicast. Real time services like Internet broadcasting are provided by the overlay multicast technology due to the complex nature and high cost of IP multicast. To reduce frequent updates of multicast members and to support real time service without delay, we suggest a reliable overlay multicast tree based on members' sojourn probabilities. Path reliabilities from a source to member nodes are considered to maximize the reliability of an overlay multicast tree. The problem is formulated as a binary integer programming with degree and delay bounds. A tabu search heuristic is developed to solve the NP-complete problem. Outstanding results are obtained which is comparable to the optimal solution and applicable in real time.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.17-26
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• 2008

The traveling salesman problem is to find tours through all cities at minimum cost ; simply visiting the cities only once that a salesman wants to visit. As such, the traveling salesman problem is a NP-complete problem ; an heuristic algorithm is preferred to an exact algorithm. In this paper, we suggest an effective cost relaxation using a candidate arc set which is obtained from a regression function for the traveling salesman problem. The proposed method sufficiently consider the characteristics of cost of arcs compared to existing methods that randomly choose the arcs for relaxation. For test beds, we used 31 instances over 100 cities existing from TSPLIB and randomly generated 100 instances from well-known instance generators. For almost every instances, the proposed method has found efficiently better solutions than the existing method.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.1-11
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• 2010

The conditional covering problem is an important variation of well studied set covering problem. In the set covering problem, the problem is to find a minimum cardinality vertex set which will cover all the given demand points. The conditional covering problem asks to find a minimum cardinality vertex set that will cover not only the given demand points but also one another. This problem is NP-complete for general graphs. In this paper, we present an efficient algorithm to solve the conditional covering problem on interval graphs with n vertices which runs in O(n)time.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.145-156
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• 1998

In a classical clustering problem, grouping is done on the basis of similarities or distances (dissimilarities) among the elements. Therefore, the objective is to minimize the variance within each group while maximizing the between-group variance among all groups. In this paper, however, a new class of clustering problem is introduced. We call this a laydown grouping problem (LGP). In LGP, the objective is to minimize both the within-group and between-group variances. Furthermore, the problem is expanded to a multi-dimensional case where the two-way minimization process must be considered for each dimension simultaneously for all measurement characteristics. At first, the problem is assessed by analyzing its variance structures and their complexities by conjecturing that LGP is NP-complete. Then, the simulated annealing (SA) algorithm is applied and the results are compared against that from others.