• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.243-246
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• 2004

배낭 문제는 단순한 것 같지만 조합형 특성을 가진 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 GA(Genetic algorithms)를 이용하였으나 지역해에 빠질 수 있어 잘못된 해를 찾거나 찾지 못하는 문제점을 갖고 있다. 본 논문에서는 이러한 문제점들을 해결하기 위해 막대한 병렬성과 저장능력을 가진 DNA 컴퓨팅 기법에 DNA에 기반한 변형된 GA인 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optmization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 GA를 이용한 것 보다 초기 문제 표현에서 우수한 적합도를 생성했으며, 빠른 시간내에 우수한 해를 찾을 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.539-542
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• 2004

배낭 문제는 조합 최적화 문제로서, 다항 시간(polynomial time)에 풀리지 않는 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 DNA 컴퓨팅 기법과 GA 등을 사용하여 해결하였다. 하지만 기존의 방법들은 DNA의 정확한 특성을 고려하지 않아, 실제 실험과의 결과 차이가 발생하고 있다. 본 논문에서는 DNA 컴퓨팅 실험 과정에서 발생하는 DNA 조작 오류를 최소화하고, 보다 정확한 예측을 위해 함수 언어인 Haskell을 이용한 코드 최적화 DNA-Haskell을 제안한다. 코드 최적화 DNA-Haskell은 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 DNA 컴퓨팅 방법보다 실험적 오류를 최소화하였으며, 또한 적합한 해를 빠른 시간 내에 찾을 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.730-735
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• 2005

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there were difficulties in real experiments because the conventional method didn't reflect the precise characteristics of DNA. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve problems of Knapsack Problem. ACO was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors as compared with conventional methods, and found accurate solutions more rapidly.

• 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 Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.137-142
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• 1996

An algorithm for solving the cardinality constrained linear programming knapsack problem is presented. The algorithm has a convenient structure for a branch-and-bound approach to the integer version, especially to the 0-1 collapsing knapsack problem. A numerical example is given.

• 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 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 the Korean Operations Research and Management Science Society
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• v.36 no.2
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• pp.43-50
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• 2011

We consider the separation problem of the rank-1 Chvatal-Gomory (C-G) inequalities for the 0-1 knapsack problem with the knapsack capacity defined by an additional binary variable, which we call the fixed-charge 0-1 knapsack problem. We analyze the structural properties of the optimal solutions to the separation problem and show that the separation problem can be solved in pseudo-polynomial time. By using the result, we also show that the existence of a pseudo-polynomial time algorithm for the separation problem of the rank-1 C-G inequalities of the ordinary 0-1 knapsack problem.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.74-79
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• 2012

An efficient separation heuristic for the rank-1 Chvatal-Gomory cuts for the binary knapsack problem is proposed. The proposed heuristic is based on the decomposition property of the separation problem for the fixedcharge 0-1 knapsack problem characterized by Park and Lee [14]. Computational tests on the benchmark instances of the generalized assignment problem show that the proposed heuristic procedure can generate strong rank-1 C-G cuts more efficiently than the exact rank-1 C-G cut separation and the exact knapsack facet generation.

• 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.