• Magazine of the Korean Society of Agricultural Engineers
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• v.27 no.4
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• pp.61-67
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• 1985

This study is concerned with the optimum design of reinforced concrete frame structure with multi-stories and multi-bays by Limit State Design Method aimed to establish a synthetical optimal method that can simultaneously acomplish structural analysis and sectional desig. For optimum solution, the Successive Linear Programming known as effective to nonlinear optimization problem: including both multi-design variables and mulit-constrained condition was applied. The developed algorithm was applied to an actual structure and reached following results. 1)The developed algorithm was rvey effective converging to an optimal solution with 3 to 5 iteration. 2)An optimal solution was showed when bending moment redistribution factor a was 0.80. 3)The column was, regardless of story, controlled by the long column when unbraced, while in case of braced column, it is designed with 3 short column controlled by thrust and bending moment, and the supporting condition had little effect on the optimization results.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.41-47
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• 2015

The bandwidth minimization problem (BMP) has been classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. This paper suggests polynomial time heuristic algorithm is to find the solution of bandwidth minimization problem. To find the minimum bandwidth ${\phi}^*=_{min}{\phi}(G)$, ${\phi}(G)=_{max}\{{\mid}f(v_i)-f(v_j):v_i,v_j{\in}E\}$ for given graph G=(V,E), m=|V|,n=|E|, the proposed algorithm sets the maximum degree vertex $v_i$ in graph G into global central point (GCP), and labels the median value ${\lceil}m+1/2{\rceil}$ between [1,m] range. The graph G is partitioned into subgroup, the maximum degree vertex in each subgroup is set to local central point (LCP), and we adjust the label of LCP per each subgroup as possible as minimum distance from GCP. The proposed algorithm requires O(mn) time complexity for label to all of vertices. For various twelve graph, the proposed algorithm can be obtains the same result as known optimal solution. For one graph, the proposed algorithm can be improve on known solution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.81-84
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• 2003

The ability to determine the optimal frequencies and offsets for independent and unrestricted ordering cycles for multiple items can be very valuable for managing storage capacity constrained facilities in a supply chain. The complexity of this problem has resulted in researchers focusing on more tractable surrogate problems that are special cases of the base problem. Murthy et al. (European Journal of Operation Research 2003) developed insights leading to solution of the original problem and present a heuristic for offsetting independent and unrestricted ordering cycles for items to minimize their joint storage requirements. However, their study cannot find optimal solution due to the Greedy Heuristic solution procedure. In this paper, we present a complete procedure to find the optimal solution for the model with a integer programming optimization approach and genetic algorithm. Numerical examples are included to compare each model with that of Murthy et at. Research of this type may prove useful in solving the more general problem of selecting order policies to minimize combined holding, ordering, and storage costs.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1193-1196
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• 1996

Genetic algorithm(GA) is useful to find optimal solution without any special mathematical modeling. This study presents to search optimal path of Autonomous Mobile Robot(AMR) by using GA without encoding and decoding procedure. Therefore, this paper shows that the proposed algorithm using GA can reduce the computation time to search the optimal path.

• Smart Structures and Systems
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• v.20 no.5
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• pp.607-618
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• 2017

In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.465-472
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• 2004

Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.1-8
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• 2016

The printed circuit board (PCB) can be used only 2 layers of front and back. Therefore, the wiring line segments are located in 2 layers without crossing each other. In this case, the line segment can be appear in both layers and this line segment is to resolve the crossing problem go through the via. The via minimization problem (VMP) has minimum number of via in layout design problem. The VMP is classified by NP-complete because of the polynomial time algorithm to solve the optimal solution has been unknown yet. This paper suggests polynomial time algorithm that can be solve the optimal solution of VMP. This algorithm transforms n-line segments into vertices, and p-crossing into edges of a graph. Then this graph is partitioned into 3-coloring sets of each vertex in each set independent each other. For 3-coloring sets $C_i$, (i=1,2,3), the $C_1$ is assigned to front F, $C_2$ is back B, and $C_3$ is B-F and connected with via. For the various experimental data, though this algorithm can be require O(np) polynomial time, we obtain the optimal solution for all of data.

• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.117-129
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• 1998

This study is concerned with developing a heuristic for a web-based vehicle routing problem using mixed genetic algorithm(VRPMGA) which determines each vehicle route in order to minimize the transportation costs, subject to meeting the demands of all delivery points. VRP is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a mixed genetic algorithm by partitioned strategy which can give a good solution in comparatively brief time. The good features of the VRPMGA are, fristly, the ability of early convergence and, secondly, the capability of producing multiple, alternative, and near-optimal solutions. The VRPMGA is a useful algorithm that can be appliable to VRP and TSP. Finally, the computational test were performed using the benchmark problems and the proposed heuristic is compared with the other existing algorithms (COSA). The result of computational tests shows that proposed heuristic gives good solutions, in much shorter time, which are same as the best known solutions in the pervious research.

• Journal of Korean Society for Quality Management
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• v.50 no.2
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• pp.265-286
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• 2022

Purpose: The time and cost of a project activity exists in a selected mode and there is a quality level for the selected mode, and the time and cost of the current activity is determined by the quality level of the preceding activity. When an activity is a predecessor activity of an activity, it is characterized as a trade-off problem in which the time and cost of the activity are determined according to the quality level of the activity. Methods: A neighbor search heuristic algorithm obtains a solution by (1) randomly determining the mode, quality level, and assignment order for each activity. (2) get a solution by improving the solution by changing the possible modes and quality levels; (3) to find a solution by improving the solution from the point where it is feasible to advance the start time. Here, Case[1] is a method to find the optimal solution value after repeating (1). Case [2] is a method for finding a solution including (1) and (2). Case [3] refers to a method for finding solutions including (1), (2), and (3). Results: It can be seen that the value of the objective function presented by the algorithm changes depending on how the model of the heuristic algorithm is designed and applied. In other words, it suggests the importance of algorithm design and proves the importance of the quality problem of activities in the project schedule. Conclusion: A study significance of the optimization algorithm and the heuristic algorithm was applied to the effect of the quality of the preceding activity on the duration and cost of itself and the succeeding activity, which was not addressed in the project schedule problem.

• ETRI Journal
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• v.15 no.3
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• pp.47-59
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• 1994

A capacity expansion planning problem with buy-or-lease decisions is considered. Demands for capacity are deterministic and are given period-dependently at each period. Capacity additions occur by buying or leasing a capacity, and leased capacity at any period is reconverted to original source after a fixed length of periods, say, lease period. All cost functions (buying, leasing and idle costs) are assumed to be concave. And shortages of capacity and disposals are not considered. The properties of an optimal solution are characterized. This is then used in a tree search algorithm for the optimal solution and other two algorithms for a near-optimal solution are added. And these algorithms are illustrated with numerical examples.