• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.225-228
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• 2004

The generalized knapsack problem, or gknap is the combinatorial optimization problem of optimizing a nonnegative linear functional over the integral hull of the intersection of a polynomially separable 0 - 1 polytope and a knapsack constraint. Among many potential applications, the knapsack, the restricted shortest path, and the restricted spanning tree problem are such examples. We prove via the ellipsoid method the equivalence between the fully polynomial approximability and a certain pseudo-polynomial separability of the gknap polytope.

• Management Science and Financial Engineering
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• v.18 no.2
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• pp.13-17
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• 2012

Set covering has widely been accepted as a staple tool for feature selection in data mining. We present a generalized version of this classical combinatorial optimization model to make it better suited for the purpose and propose a surrogate relaxation-based procedure for its meta-heuristic solution. Mathematically and also numerically with experiments on 25 set covering instances, we demonstrate the utility of the proposed model and the proposed solution method.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.1
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• pp.25-35
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• 2013

Automatically deriving only individual don't-care minterms that can effectively reduce a Boolean logic expressions are being investigated. Don't-care conditions play an important role in optimizing logic design. The type of unknown don't-care minterms that can always reduce the number of product terms in Boolean expression are referred as single hypothetical don't-care (S-HDC) minterms. This paper describes the Quasi Quine-McCluskey method that systematically derives S-HDC minterms. For the most part, this method is similar to the original Quine-McCluskey method in deriving the prime implicants. However, the Quasi Quine-McCluskey method further derives S-HDC minterms by applying so-called a combinatorial comparison operation. Upon completion of the procedure, the designer can review generated S-HDC minterms to test its appropriateness for a particular application.

• Macromolecular Research
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• v.17 no.11
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• pp.829-841
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• 2009

This study introduces a specified group-contribution method for predicting the phase equilibria in polymer solutions. The method is based on a modified double lattice model developed previously. The proposed model includes a combinatorial energy contribution that is responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, a polar force and specific energy contribution. Using the group-interaction parameters obtained from data reduction, the solvent activities for a large variety of mixtures of polymers and solvents over a wide range of temperatures can be predicted with good accuracy. This method is simple but provides improved predictions compared to those of the other group contribution methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.105-114
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• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.1
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• pp.43-56
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• 2004

The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved In the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.124-126
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• 2002

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, the method employing the chaos search algorithm is proposed to solve optimal capacitor placement problem with reducing computational effort and enhancing optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.213-220
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• 2008

The object of this paper is to propose a method to deal with the problem of modeling user specifications in approaches based on supervisory control and Petri nets. However, most of Petri Net approaches are based on forbidden states specifications, and these specifications are suitable the use of tool such as the reachability graph. But these methods were not able to show the user specification easily and these formalisms are generally limited by the combinatorial explosion that occurs when attempting to model complex systems. Herein, we propose a new efficient method using FSSTP (Forbidden Sequences of State-Transitions Problem) and theory of region. Also, to detect and avoid the deadlock problem in control process, we use DAPN method (Deadlock Avoidance Petri nets) for solving this problem in control model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.