• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.316-321
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• 2007

The hierarchical covering location problem emphasizes the issue of locating of hierarchical facilities in order to maximize the number of customers that can be covered. In the classical HCLP(Hierarchical Covering Location Problem), it is assumed that the customers are covered completely if they are located within a specific distance from the facility, and not covered otherwise. The generalized HCLP is introduced that the coverage of customers is measured to be any real value rather than 0 or 1, where the service level may decrease according to the distance. Mixed integer programming formulation for the generalized HCLP is suggested with a partial coverage of service. Solutions are found using OPL Studio, and are evaluated for various cases.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.2
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• pp.97-104
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• 1990

The problem is a variation of the weighted set-covering problem (SCP) which requires the minimum-cost cover to be self-covering. It is shown that direct extension of the well-known greedy heuristic for SCP can have an arbitrarily large error in the worst case. It remains an open question whther these exists a greedy heuristic with a finite error bound.

• Honam Mathematical Journal
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• v.31 no.3
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• pp.279-292
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• 2009

As a survey-type article, the paper reviews some results on a regular covering space in digital covering theory. The recent paper [10](see also [12]) established the notion of regular covering space in digital covering theory and studied its various properties. Besides, the papers [14, 16] developed a discrete Deck's transformation group of a digital covering. In this paper we study further their properties. By using these properties, we can classify digital covering spaces. Finally, the paper proposes an open problem.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.13-21
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• 2014

The set covering problem (SCP) is one of representative combinatorial optimization problems, which is defined as the problem of covering the m-rows by a subset of the n-columns at minimal cost. This paper proposes a method utilizing Integer Programming-based Local Search (IPbLS) to solve the set covering problem. IPbLS is a kind of local search technique in which the current solution is improved by searching neighborhood solutions. Integer programming is used to generate neighborhood solution in IPbLS. The effectiveness of the proposed algorithm has been tested on OR-Library test instances. The experimental results showed that IPbLS could search for the best known solutions in all the test instances. Especially, I confirmed that IPbLS could search for better solutions than the best known solutions in four test instances.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.189-199
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• 1994

This paper considers the covering network design problem (CNDP). In the CNDP, an undirected graph is given where nodes correspond to potential facility sites and arcs to potential links connecting facilities. The objective of the CNDP is to identify the least cost connected subgraph whose nodes cover the given demand points. The problem difines a demand point to be covered if some node in the selected graph is present within an appropriate distance from the demand point. We present an integer programming formulation for the problem and develop a dual-based solution procedure. The computational results for randomly generated test problems are also shown.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.139-155
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• 2006

The facility location problem of this paper is distinguished from the maximal covering location problem and the flxed-charge facility location problem. We propose the maximal profiting location problem (MPLP) that is the facility location problem maximizing profit with multi-product. We apply to the simulated annealing algorithm, the stochastic evolution algorithm and the accelerated simulated annealing algorithm to solve this problem. Through a scale-down and extension experiment, the MPLP was validated and all the three algorithm enable the near optimal solution to produce. As the computational complexity is increased, it is shown that the simulated annealing algorithm' is able to find the best solution than the other two algorithms in a relatively short computational time.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.4
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• pp.87-109
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• 2002

A satisfiability problem in propositional logic is the problem of checking for the existence of a set of truth values of atomic prepositions that renders an input propositional formula true. This paper describes an empirical investigation of a particular integer programming approach, using the set covering model, to solve satisfiability problems. Our satisfiability engine, SETSAT, is a fully integrated, linear programming based, branch and bound method using various symbolic routines for the reduction of the logic formulas. SETSAT has been implemented in the integer programming shell MINTO which, in turn, uses the CPLEX linear programming system. The logic processing routines were written in C and integrated into the MINTO functions. The experiments were conducted on a benchmark set of satisfiability problems that were compiled at the University of Ulm in Germany. The computational results indicate that our approach is competitive with the state of the art.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.121-135
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• 2013

This paper propose an efficient algorithm for selecting electric bus charging facility location. In nature, the optimal charging facility location problem is similar to Set Covering Problem. Set Covering Problem is the problem of covering all the rows of an $m{\times}n$ matrix of ones and zeros by a subset of columns with a minimal cost. It has many practical applications of modeling of real world problems. The Set Covering Problem has been proven to be NP-Complete. In order to overcome the computational complexity involved in seeking optimal solutions, this paper present an enhanced greedy algorithm and simulated annealing algorithm. In this paper, we apply the developed algorithm to Seoul's public bus system.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.570-576
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• 2004

It is very difficult to efficiently solve a large-scaled maximal covering problem(MCP) by a genetic algorithm. In this paper, we present new crossover and mutation operators specially designed for genetic algorithms to solve large-scaled MCPs efficiently. We also introduce a novel genetic algorithm employing unexpressed genes. Unexpressed genes are the genes which are not expressed and thus do not affect the evaluation of the individuals. These genes play the role of reserving information susceptible to be lost by the application of genetic operations but is suspected to be potentially useful in later generations. The genetic algorithm employing unexpressed genes enjoys the advantage of being able to maintain diversity of the population and thus can search more efficiently to solve large-scaled MCPs. Experiments with large-scaled real MCP data has shown that our genetic algorithm employing unexpressed genes significantly outperforms tabu search which is one of the popularly used local neighborhood search algorithms for optimization.