• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.31-41
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• 1992

An extension of generalized linear multiple choice knapsack problem [1] is presented and an algorithm of order 0([n .n$_{max}$]$_{2}$) is developed by exploiting its extended properties, where n and n$_{max}$ denote the total number of variables and the cardinality of the largest multiple choice set, respectively. A numerical example is presented and computational aspects are discussed.sed.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.365-375
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• 1996

We consider an extension of the multiple choice linear knapsack problem and develop a fast algorithm of order $O(r_{max}n^2)$ by exploiting some new properties, where $r_{max}$ is the largest multiple choice number and n is the total number of variables. The proposed algorithm has convenient structures for the post-optimization in changes of the right-hand-side and multiple choice numbers. A numerical example is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.291-299
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• 1998

We present a variant for the generalized continuous multiple-choice knapsack problem[1], which additionally has the well-known generalized lower bound constraints. The presented problem is characterized by some variables which only belong to the simple upper bound constraints and the others which are partitioned into both the continuous multiple-choice constraints and the generalized lower bound constraints. By exploiting some extended structural properties, an efficient algorithm of order Ο($n^2$1og n) is developed, where n is the total number of variables. A numerical example is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.7 no.10
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• pp.29-33
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• 1984

Many methods have been developed to get a good Computation steps. I think that almost methods of them have been solved by using a theory of [Vj]. But I have thought that it Can be solved by an other method. This method is a way to get a Computations steps by using [Aj] instead of [Vj]. It requires less Computation time than [Vj]. So I think that method is an efficient Algorithm about "the Development of Algorithm method for the 0 - 1 Knapsack problem."

• Journal of Korean Institute of Industrial Engineers
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• v.9 no.1
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• pp.3-6
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• 1983

The objective of this paper is to present a new method for the multi-dimensional Knapsack problem. Toyoda method and Loulou and Michaelides method are well known for this problem. The new method introduces a new penalty factor for fast convergence and a branching technique for accurate solutions. The method is tested at IBM370 and shows that the method is slower than Toyoda method, but more accurate than other two methods.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.519-527
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• 1995

By finding some new properties, we develop an O($r_{max}n^2$) algorithm for the generalized multiple choice linear knapsack problem where $r_{max}$ is the largest multiple choice number and n is the total number of variables. The proposed algorithm can easily be embedded in a branch-and-bound procedure due to its convenient structure for the post-optimization in changes of the right-hand-side and multiple choice numbers. A numerical example is presented.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.173-176
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• 2014

구동에 필요한 다수의 Virtual Machine을 물리적 서버 안에 Consolidation하게 구성하면, 물리적 서버의 개수를 최소화시켜 에너지 소모를 줄일 수 있다. 이 논문에서는, 하드웨어 요구량에 따른 Virtual Machine Consolidation과 시간 패턴에 따른 Virtual Machine Consolidation을 Energy Saving 관점으로 비교하고, 에너지 효율적인 Virtual Machine Consolidation 알고리즘을 제안한다.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.327-338
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• 2013

Effective and efficient selection of IT projects is crucial for company's competitiveness. The selection of IT projects usually involves consideration of budget constraints but existing IT project selection models often neglect budget constraints. This paper presents an IT project selection model which considers budget constraints. AHP(Analytic Hierarchy Process) and Knapsack problem model have been combined to develop the proposed model, AHP-K model, where AHP is used to estimate weights of selection criteria and, then, a knapsack problem model is utilized to optimize selection of IT project while meeting the budget constraints. In this paper, a case study is provided to validate the effectiveness of the proposed AHP-K model. It has been shown that the proposed AHP-K model is better than the AHP model in terms of total utility of projects and investment efficiency.

• The Journal of Information Technology
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• v.5 no.4
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• pp.129-137
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• 2002

In this paper, a public key knapsack cryptosystem algorithm is based on the security to a difficulty of polynomial factorization in computer communication networks is proposed. For the proposed public key knapsack cryptosystem, a polynomial vector Q(x,y,z) is formed by transform of superincreasing vector P, a polynomial g(x,y,z) is selected. Next then, the two polynomials Q(x,y,z) and g(x,y,z) is decided on the public key. The enciphering first selects plaintext vector. Then the ciphertext R(x,y,z) is computed using the public key polynomials and a random integer $\alpha$. For the deciphering of ciphertext R(x,y,z), the plaintext is determined using the roots x, y, z of a polynomial g(x,y,z)=0 and the increasing property of secrety key vector. Therefore a public key knapsack cryptosystem is based on the security to a difficulty of factorization of a polynomial g(x,y,z)=0 with three variables. The propriety of the proposed public key cryptosystem algorithm is verified with the computer simulation.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.71-77
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• 2019

This paper defines a multi-selection knapsack problem and presents an algorithm for seeking its optimal solution. Multi-selection means that all members of the particular group be selected or excluded. Our branch-and-bound algorithm introduces a simplex containing the feasible region of the original problem to exploit the fact that the most tightly underestimating function on the simplex is linear. In bounding operation, the subproblem defined over the candidate simplex is minimized. During the branching process the candidate simplex is splitted into two one-less dimensional subsimplices by being projected onto two hyperplanes. The approach of this paper can be applied to solving the global minimization problems under various types of the knapsack constraints.