• Design & Manufacturing
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• v.17 no.2
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• pp.62-69
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• 2023

In this study, we conducted a study on the optimization of injection molding conditions to minimize deformation of plastic product. The charging management system housing of the vehicle was selected as the research subject. Melting temperature, cooling temperature, packing time, and packing pressure were selected as the main factors expected to affect the deformation of molded products. Each main factor was divided into 5 levels. Optimization of injection molding conditions to minimize deformation was performed using the Taguchi Method. We performed an analysis of variance (ANOVA) to identify significant factors affecting the deformation of plastic product. In order to select injection molding conditions that minimize deformation of plastic products, injection molding analysis was additionally performed for insignificant factors. We then compared the deformation of the molded part before and after optimization. As a result of comparing the injection analysis results of the basic conditions and the injection analysis results of the optimal conditions, it was confirmed that the amount of deformation after optimization was improved by about 10.9%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.7 s.250
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• pp.841-848
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• 2006

Many researchers are developing computational prediction methods for protein tertiary structures to get much more information of protein. These methods are very attractive on the aspects of breaking technologies of computer hardware and simulation software. One of the computational methods for the prediction is a fragment assembly method which shows good ab initio predictions at several cases. There are many barriers, however, in conventional fragment assembly methods. Argues on protein energy functions and global optimization to predict the structures are in progress fer example. In this study, a new prediction method for protein structures is proposed. The proposed method mainly consists of two parts. The first one is a fragment assembly which uses very shot fragments of representative proteins and produces a prototype of a given sequence query of amino acids. The second one is a global optimization which folds the prototype and makes the only protein structure. The goodness of the proposed method is shown through numerical experiments.

• 한국금형공학회:학술대회논문집
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• 2008.06a
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• pp.203-208
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• 2008

Recently, the modern society in development and industrial growth is investing a lot of time and much effort to improvement and environment of life quality. Thus, the casting tape which uses environmentally friendly and human body friendly water hardening process Polymer is rapidly substituted for the gypsum cast product which has been plentifully used in medical treatment. Until currently, prior researches have a tendency to focusing the analysis about chemical creation expense and reaction quality rather than the issue about optimization of the process for this polymerization. In the polymerization process which has been accomplished with actual same chemical creation expense, there has been a problem which is the possibility of getting a different result. This is because the optimization of respectively control factors is not accomplished which affect at polymerization process. Therefore, this research sees the chemical qualities of casting tape Polymer, consequently selects the polymerization process which is suitable. And, by using a experimental design, this research will evaluate the affects which the respective factors have on remaining NCO and viscosity. futhermore, this research will carry out the process optimization which can get the above results.

• Smart Structures and Systems
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• v.18 no.3
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• pp.425-448
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• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

• The KSFM Journal of Fluid Machinery
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• v.14 no.1
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• pp.66-76
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• 2011

The heat transfer and pressure loss characteristics of a rotating two-pass channel with a guide vane in the turning region have been studied using three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis, and the shape of the guide vane has been optimized using surrogate modeling optimization technique. For the optimization, thickness, location and angle of the guide vanes have been selected as design variables. The objective function has been defined as a linear combination of the heat transfer and the friction loss related terms with a weighting factor. Latin hypercube sampling has been applied to determine the design points as design of experiments. A weighted-average surrogate model, PBA has been used as the surrogate model. The guide vane in the turning region does not influence the heat transfer in the first passage upstream of the turning region, but enhances largely the heat transfer in the turning region and the second passage. In an example of the optimization, the objective function has been increased by 13.6%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1312-1320
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• 2009

This paper presents an efficient design method of 2-D infinite impulse response(IIR) digital filter based on a particle swarm optimization(PSO) algorithm. The design task is reformulated as a constrained minimization problem and is solved by our newly developed PSO algorithm. To ensure the stability of the designed 2-D IIR digital filters, a new stability strategy is embedded in the basic PSO algorithm. The superiority of the proposed method is demonstrated by several experiments. The results show that the approximation error of the resultant filters are better than those of the digital filters which designed by recently published filter design methods. The proposed design method can also obtain the stable2-D IIR digital filters.

• Journal of electromagnetic engineering and science
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• v.17 no.4
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• pp.208-220
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• 2017

This paper proposes an experimental optimization method for a wireless power transfer (WPT) system. The power transfer characteristics of a WPT system with arbitrary loads and various types of coupling and compensation networks can be extracted by frequency domain measurements. The various performance parameters of the WPT system, such as input real/imaginary/apparent power, power factor, efficiency, output power and voltage gain, can be accurately extracted in a frequency domain by a single passive measurement. Subsequently, the design parameters can be efficiently tuned by separating the overall design steps into two parts. The extracted performance parameters of the WPT system were validated with time-domain experiments.