• 한국정보시스템학회지:정보시스템연구
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• 제25권1호
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• 한국경영과학회지
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• 제6권1호
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• pp.65-70
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• 1981

L.G. Khachiyan's algorithm for solving a system of strict (or open) linear inequalities with integral coefficients is described. This algorithm is based on the construction of a sequence of ellipsoids in R$^n$ of decreasing n-dimensional volume and contain-ing feasible region. The running time of the algorithm is polynomial in the number of bits of computer core memory required to store the coefficients. It can be applied to solve linear programming problems in polynomially bounded time by the duality theorem of the linear programming problem. But it is difficult to use in solving practical problems. Because it requires the computation of a square roots, besides other arithmatic operations, it is impossible to do these computations exactly with absolute precision.

• Journal of applied mathematics & informatics
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• 제35권1_2호
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• pp.191-204
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• 2017

A new fifth-order weighted Runge-Kutta algorithm based on heronian mean for solving initial value problem in ordinary differential equations is considered in this paper. Comparisons in terms of numerical accuracy and size of the stability region between new proposed Runge-Kutta(5,5) algorithm, Runge-Kutta (5,5) based on Harmonic Mean, Runge-Kutta(5,5) based on Contra Harmonic Mean and Runge-Kutta(5,5) based on Geometric Mean are carried out as well. The problems, methods and comparison criteria are specified very carefully. Numerical experiments show that the new algorithm performs better than other three methods in solving variety of initial value problems. The error analysis is discussed and stability polynomials and regions have also been presented.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.431-434
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• 2003

In this paper, we propose an efficient method not only f3r producing accurate region segmentation, solving the over-segmentation problem of watershed algorithm but also f3r reducing post-processing time by reducing computation loads. Through this proposed method, region segmentation of neighboring objects and discrimination of similar intensities were effectively obtained. Input image of watershed algorithm has used the derivative-based detectors such as Sobel and Canny. But proposed method uses the pixels-similarity-based detector, that is, SUSAN. By adopting this proposed method, we can reduce the noise problem and solve the problem of over-segmentation and not lose the edge information of objects. We also propose Zero-Crossing SUSAN. With Zero-Crossing SUSAN, the edge localization, times and computation loads can be improved over those obtained from existing SUSAN

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.168-170
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• 2016

Drone package delivery routing problem is realistic problem used to find efficient route of drone package delivery service. In this paper, we present an approach for solving drone routing problem for package delivery service using two different heuristics algorithms, genetic and nearest neighbor. We implement and analyze both heuristics algorithms for solving the problem efficiently with respect to cost and time. The respective experimental results show that for the range of customers 10 to 50 nearest neighbor and genetic algorithms can reduce the tour length on average by 34% and 40% respectively comparing to FIFO algorithm.

• Journal of applied mathematics & informatics
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• 제26권5_6호
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• pp.1233-1245
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• 2008

In this paper, an iterative method is proposed to solve the least-squares problem of matrix equation AXB+CYD=E over unknown matrix pair [X, Y]. By this iterative method, for any initial matrix pair [$X_1,\;Y_1$], a solution pair or the least-norm least-squares solution pair of which can be obtained within finite iterative steps in the absence of roundoff errors. In addition, we also consider the optimal approximation problem for the given matrix pair [$X_0,\;Y_0$] in Frobenius norm. Given numerical examples show that the algorithm is efficient.

• 한국건설관리학회논문집
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• 제8권6호
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• pp.235-245
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• 2007

This research study presents the development and application of an Elitist Genetic Algorithm (Elitist GA) for solving the resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. Main features of the developed algorithm are that the elitist roulette selection operator is developed to preserve the best individual solution for the next generation so as to obtain the improved solution, and that parallel schedule generation scheme is used to generate a feasible solution to the problem. The experimental results on standard problem sets indicate that the proposed algorithm not only produces reasonably good solutions to the problems over the heuristic method and other GA, but also can find the optimal and/or near optimal solutions for the large-sized problems with multiple resources within a reasonable amount of time that will be applicable to the construction industry. This paper will help researchers and/or practitioners in the construction project scheduling software area with alternative means to find the optimal schedules by utilizing the advantages of the Elitist GA.

• Journal of applied mathematics & informatics
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• 제40권5_6호
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• pp.933-947
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• 2022

In this research, we presented the type 2 fuzzy transportation problem with additional constraints and solved by our proposed genetic algorithm model, and the results are verified using the softwares, genetic algorithm tool in Matlab and Lingo. The goal of our approach is to minimize the cost in solving a transportation problem with an additional constraint (TPAC) using the genetic algorithm (GA) based type 2 fuzzy parameter. We reduced the type 2 fuzzy set (T₂FS) into a type 1 fuzzy set (T₁FS) using a critical value-based reduction method (CVRM). Also, we use the centroid method (CM) to obtain the corresponding crisp value for this reduced fuzzy set. To achieve the best solution, GA is applied to TPAC in type 2 fuzzy parameters. A real-life situation is considered to illustrate the method.

• 경영과학
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• 제22권1호
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• pp.167-178
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• 2005

The 2DBPP(Two-Dimensional Bin Packing Problem) is a problem of packing each item into a bin so that no two items overlap and the number of required bins is minimized under the set of rectangular items which may not be rotated and an unlimited number of identical .rectangular bins. The 2DBPP is strongly NP-hard and finds many practical applications in industry. In this paper we discuss a tabu search approach which includes tabu list, intensifying and diversification Strategies. The HNFDH(Hybrid Next Fit Decreasing Height) algorithm is used as an internal algorithm. We find that use of the proper parameter and function such as maximum number of tabu list and space utilization function yields a good solution in a reduced time. We present a tabu search algorithm and its performance through extensive computational experiments.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.947-951
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• 2005

The concept of group technology has been successfully applied to many production systems including flexible manufacturing systems. In this paper we apply group technology principles to the economic lot scheduling problem which has been intensively studied over 40 years. We obtain a production schedule of several family products on a single facility where setup times and costs can be reduced by using the concept of group technology. We develop a heuristic algorithm and a hybrid genetic algorithm for the group technology economic lot scheduling problem (GT-ELSP). Numerical example shows that the developed heuristic and the hybrid genetic algorithm outperform the existing heuristics.