• International conference on construction engineering and project management
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• 2022.06a
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• pp.608-615
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• 2022

Construction projects are large-scale projects that require extensive construction costs and resources. Especially, scheduling is considered as one of the essential issues for project success. However, the schedule and resource management are challenging to conduct in high-tech construction projects including complex design of MEP and architectural finishing which has to be constructed within a limited workspace and duration. In order to deal with such a problem, this study suggests resource and sequence optimization using constraint programming in construction projects. The optimization model consists of two modules. The first module is the data structure of the schedule model, which consists of parameters for optimization such as labor, task, workspace, and the work interference rate. The second module is the optimization module, which is for optimizing resources and sequences based on Constraint Programming (CP) methodology. For model validation, actual data of plumbing works were collected from a construction project using a five-minute rate (FMR) method. By comparing actual data and optimized results, this study shows the possibility of reducing the duration of plumbing works in construction projects. This study shows decreased overall project duration by eliminating work interference by optimizing resources and sequences within limited workspaces.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.351-368
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• 2014

This study presents a hunting search based optimum design algorithm for engineering optimization problems. Hunting search algorithm is an optimum design method inspired by group hunting of animals such as wolves, lions, and dolphins. Each of these hunters employs hunting in a different way. However, they are common in that all of them search for a prey in a group. Hunters encircle the prey and the ring of siege is tightened gradually until it is caught. Hunting search algorithm is employed for the automation of optimum design process, during which the design variables are selected for the minimum objective function value controlled by the design restrictions. Three different examples, namely welded beam, cellular beam and moment resisting steel frame are selected as numerical design problems and solved for the optimum solution. Each example differs in the following ways: Unlike welded beam design problem having continuous design variables, steel frame and cellular beam design problems include discrete design variables. Moreover, while the cellular beam is designed under the provisions of BS 5960, LRFD-AISC (Load and Resistant Factor Design-American Institute of Steel Construction) is considered for the formulation of moment resisting steel frame. Levy Flights is adapted to the simple hunting search algorithm for better search. For comparison, same design examples are also solved by using some other well-known search methods in the literature. Results reveal that hunting search shows good performance in finding optimum solutions for each design problem.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.373-378
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• 2001

The optimization method based on an enhanced genetic algorithms is for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is a global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by single point method in reconstructive search space. Four numerical examples are also presented in this papers to comparing with conventional methods.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.217-225
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• 2023

2,3-Butanediol (2,3-BDO), which is used in various fields such as cosmetics and fertilizers, is a high value-added substance and the demand for it is gradually increasing. 2,3-BDO produced from the fermentation of microorganisms not only contains by-products of fermentation, but also varies greatly in feed composition depending on fermentation conditions, so it is difficult to efficiently operate the separation process to reach the target purity of the product. Therefore, in this study, through dynamic optimization of the bio-based 2,3-BDO distillation process, the optimal control route was explored to control the 2,3-BDO concentration of the bottom product to 99 wt% or more, when feed concentration changes. Steady and dynamic state process simulation, proportional integral (PI) controller design, and dynamic optimization were sequentially performed. As a result, the error between the 2,3-BDO concentration and the set point of the bottom product was reduced by 75.2%.

• Journal of the Optical Society of Korea
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• v.20 no.2
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• pp.283-290
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• 2016

This study presents an 8x four-group inner-focus zoom lens with one-moving group for a compact camera by use of a focus tunable lens (FTL). In the initial design stage, we obtained the powers of lens groups by paraxial design based on thin lens theory, and then set up the zoom system composed of four lens modules. Instead of numerically analytic analysis for the zoom locus, we suggest simple analysis for that using lens modules optimized. After replacing four groups with equivalent thick lens modules, the power of the fourth group, which includes a focus tunable lens, is designed to be changed to fix the image plane at all positions. From this design process, we can realize an 8x four-group zoom system having one moving group by employing a focus tunable lens. The final designed zoom lens has focal lengths of 4 mm to 32 mm and apertures of F/3.5 to F/4.5 at wide and tele positions, respectively.

• Architectural research
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• v.11 no.2
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• pp.19-26
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• 2009

The effect of rebirthing technique on the genetic algorithm (GA)-based size optimization is investigated. The GA mimics the principles of nature and it can gradually improve structural design through biological operations such as fitness, selection, crossover and mutation. However, premature optimum has been often detected in the generic GA with continuous design variable. Since then, the so-called rebirthing technique has been proposed to avoid this problem. However, the performance of the rebirthing technique has not been reported. Therefore, the size optimizations of spatial structures are tackled to investigate the performance of the rebirthing technique on the generic GA. From numerical results, it is well proved that the rebirthing technique is very effective to produce the optimum values regardless of the values of parameters used in the GA operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3534-3549
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• 2020

The traditional method of smoke image recognition has low accuracy. For this reason, we proposed an algorithm based on the good group of IMFOA which is GMFOA to optimize the parameters of SVM. Firstly, we divide the motion region by combining the three-frame difference algorithm and the ViBe algorithm. Then, we divide it into several parts and extract the histogram of oriented gradient and volume local binary patterns of each part. Finally, we use the GMFOA to optimize the parameters of SVM and multiple kernel learning algorithms to Classify smoke images. The experimental results show that the classification ability of our method is better than other methods, and it can better adapt to the complex environmental conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.119-125
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• 2011

The part family (PF) formation in a cellular manufacturing has been a key issue for the successful implementation of Group Technology (GT). Basically, a part has two different attributes; i.e., design and manufacturing. The respective similarity in both attributes is often conflicting each other. However, the two attributes should be taken into account appropriately in order for the PF to maximize the benefits of the GT implementation. This paper proposes a clustering algorithm which considers the two attributes simultaneously based on pareto optimal theory. The similarity in each attribute can be represented as two individual objective functions. Then, the resulting two objective functions are properly combined into a pareto fitness function which assigns a single fitness value to each solution based on the two objective functions. A GA is used to find the pareto optimal set of solutions based on the fitness function. A set of hypothetical parts are grouped using the proposed system. The results show that the proposed system is very promising in clustering with multiple objectives.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.72-79
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• 2010

Device-to-device(D2D) links which share resources in a cellular network present a challenge in radio resource management due to the potentially severe interference they may cause to the cellular network. In this paper, a resource allocation scheme based on subset reuse methods is proposed to minimize the interference from the D2D links. We consider an adaptive group-wise subset reuse method to enhance the efficiency of frequency resource allocation for cellular and D2D links. A power optimization scheme is also proposed for D2D links if cellular links are interfered by adjacent D2D transmissions. The computer simulation results show that performance gain is obtained in link SINR, and total cell throughput increases as nearby traffic becomes more dominant.