• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.96-101
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• 2004

In this paper, we propose an effective cut generation method based on the Chvatal-Gomory procedure for a variable-capacity (0,1)-Knapsack problem, which is the same problem as the ordinary binary knapsack problem except that a binary capacity variable is newly introduced. We first derive a class of valid inequalities for the problem using Chvatal-Gomory procedure, then analyze the associated separation problem. Based on the results, we show that there exists a pseudopolynomial time algorithm to solve the separation problem. Preliminary computational results are presented which show the effectiveness of the proposed cut generation method.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.2
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• pp.33-44
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• 1990

An efficient algorithm is developed for the linear programming relaxation of generalized multiple choice knaspack problem. The generalized multiple choice knaspack problem is an extension of the multiple choice knaspack problem whose relaxed LP problem has been studied extensively. In the worst case, the computational coimplexity of the proposed algorithm is of order 0(n. $n_{max}$)$^{2}$), where n is the total number of variables and $n_{max}$ denotes the cardinality of the largest multiple choice set. The algorithm can be easily embedded in a branch-and-bound procedure for the generalized multiple choice knapsack problem. A numerical example is presented and computational aspects are discussed.sed.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.755-760
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• 2010

In general, Lagrangian method for discrete optimization is a kind of technique to easily manage constraints. It is traditionally used for finding upper bounds in the branch-and-bound method. In this paper, we propose a new Lagrangian search method for the 0-1 knapsack problem with multiple constraints. A novel feature of the proposed method different from existing Lagrangian approaches is that it can find high-quality lower bounds, i.e., feasible solutions, efficiently based on a new property of Lagrangian vector. We show the performance improvement of the proposed Lagrangian method over existing ones through experiments on well-known large scale benchmark data.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.1-15
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• 2000

In this paper, we propose a practical cut generation method based on the Chvatal-Gomory procedure for the (0, 1)-Knapsack problem with a variable capacity. For a given set N of n items each of which has a positive integral weight and a facility of positive integral capacity, a feasible solution of the problem is defined as a subset S of N along with the number of facilities that can satisfy the sum of weights of all the items in S. We first derive a class of valid inequalities for the problem using Chvatal-Gomory procedure, then analyze the associated separation problem. Based on the results, we develop an affective cut generation method. We then analyze the theoretical strength of the inequalities which can be generated by the proposed cut generation method. Preliminary computational results are also presented which show the effectiveness of the proposed cut generation method.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.62
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• pp.21-32
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• 2001

The two-dimensional guillotine cutting problem is to maximize sum of piece profits that cut from one stock rectangle and widely applied in the industry. The branch-and-bound method for this problem uses complementarily several upper bounds(the Gilmore and Gomoryp[8]'s two-dimensional knapsack function and the Hifi and Zissimopoulos[10]'s method using one-dimensional knapsack problem, etc) to reduce the number of searched nodes. These upper bounds has a shortcoming that does not consider the bound and layout of pieces simultaneously. In this paper, we propose an efficient upper bound which can complement the shortcoming of existing upper bounds. The proposed upper bound needs less memory spaces and computing time. Computational results show that the proposed upper bound significantly contribute to reduce the computational amount of time and number of searched nodes in tree.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.481-484
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• 2013

Many people today are interested in Machine to Machine (M2M) as the new-generation growth engine of mobile communications service providers in Korea. Its use and application are broadening to various areas and the number of devices used for it is increasing to increase the number of information transmitted for each object. When the traffic reaches its limit while each set of data is transmitted from the devices divided into each group through the mobile network, M2M communications service might not be processed smoothly. This study used the Knapsack Problem algorithm to create a virtual simulator for a smooth M2M service when the mobile network used for the M2M communications reaches its limit. The virtual simulator applies smooth processing of services from the M2M communications that should be processed first to other subsequent services when data comes to each group of devices. As the M2M technology develops to make many objects more compact in size, it would help with smoother processing of M2M services for the mobile network with fast-increasing traffic.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.142-148
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• 2012

Release planning for incremental software development is to select and assign features in sequence of releases along a specified planning horizon. It includes the technical precedence inherent in the features, the conflicting priorities as determined by the representative stakeholders, and the balance between required and available resources. The complexity of this consideration is getting more complicated when planning releases in software product lines. The problem is formulated as a precedence-constrained multiple 0-1 knapsack problem. In this research a genetic algorithm is developed for solving the release planning problems in software product lines as well as tests for the proposed solution methodology are conducted using data generated randomly.

• Review of KIISC
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• v.9 no.4
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• pp.81-87
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• 1999

Density'(밀도)가 비교적 높은 Chor-Rivest 암호체계는 기존의 LLL과 같은 유형의 공격법이 아니라 비밀키를 일부 찾아내므로 써 공격이 가능하고 '98 Crypto에 처음 발표되 고 '99 Crypto에 그의 공격법과 안전성이 논의된 hidden subset sum problem은 기존의 knapsack 유형의 암호체계와 마찬가지로 밀도가 높을 때 안전하고 밀도가 낮으면 공격이 가능하다 따라서 두 암호체계의 접목을 통하여 안전한 암호체계가 가능한지를 살펴보는 것 도 의미가 있을 것이다, 결론적으로 이야기하면 두암호체계의 접목은 여러 가지 문제점을 포함하고 있기 때문에 어려우리라 생각된다. 제1장에서의 hidden subset sum problem을 살 펴보고 제2장에서는 Chor-Rivest 암호체계를 분석해보고 제 3장에서 Chor-Rivest 암호체계 의 변경 가능한 요소들을 살펴보고 제4장에서 Chor-Rivest 암호체계에 hidden subset sum problem의 활용이 가능한지를 살펴보도록한다. knapsack 유형의 암호체계들중 비교적 최근 까지 안전하다고 하는 암호체계들을 살펴봄으로써 이런 유형들의 개발여부를 생각해 볼수 있는 기회가 되리라 기대된다.