• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.1-8
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• 2010

A vendor selection problem consists of two different kinds of decision making. First one is to choose the best suppliers among all possible suppliers and the next is to allocate the optimal quantities of orders among the selected vendors. In this study, an integration of the technique for order preference by similarity to ideal solution (TOPSIS) and a multi-objective mixed integer programming (MOMIP) is developed to account for all qualitative and quantitative factors which are used to evaluate and choose the best group of vendors and to decide the optimal order quantity for each vendor. A solution methodology for the vendor selection model of multiple-vendor, multiple-item with multiple decision criteria and in respect to finite vendor capacity is presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.5-8
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• 2003

This paper presents a methodology to be able to select an appropriate RP system that suits the end use of a part. Evaluation factors used in process selection include major attributes such as accuracy, roughness, strength, elongation, part cost and build time that greatly affect the performance of RP systems. Crisp values such as accuracy and surface roughness are obtained with a new test part developed. The test part is designed with conjoint analysis to reflect users' preference. The part cost and build time that have approximate ranges due to cost and many variable parameters are presented by linguistic values that can be described with triangular fuzzy numbers. Based on the evaluation values obtained, an appropriate RP process for a specific part application is selected by using the modified TOPSIS(Technique of Order Preference by Similarity to Ideal Solution) method. It uses crisp data as well as linguistic variables, and each weight on the alternatives is assigned by using pair-wise comparison matrix. The ranking order helps the decision making of the selection of RP systems.

• Korean Management Science Review
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• v.14 no.2
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• pp.1-17
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• 1997

Many companies in these days have pursued an outsourcing policy to survive in a highly competitive world market. To effectively deal with the outsourcing policy, it is required that the companies have a useful method to rationally evaluate a supplier's performance from a viewpoint of a company's strategy and in a comparative-integrated manner. In this paper, we examined the relative importance of supplier selection criteria for a company's strategy by conducting a survey and proposed "Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)" as one of the plausible techniques to evaluate a supplier's total performance. A hypothetical case study is presented to demonstrate the applicability of the method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.161-169
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• 2016

This paper is intended to propose a novel approach to select an optimal site for a small-scaled artificial recharge system installation using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) with geospatial data. TOPSIS is a MCDM (Multi-Criteria Decision Making) method to choose the preferred one of derived alternatives by calculating the relative closeness to an ideal solution. For applying TOPSIS, in the first, the topographic shape representing optimal recovery efficiency is defined based on a hydraulic model experiment, and then an appropriate surface slope is determined for the security of a self-purification capability with DEM (Digital Elevation Model). In the second phase, the candidate areas are extracted from an alluvial map through a morphology operation, because local alluvium with a lengthy and narrow shape could be satisfied with a primary condition for the optimal site. Thirdly, a shape file over all candidate areas was generated and criteria and their values were assigned according to hydrogeologic attributes. Finally, TOPSIS algorithm was applied to a shape file to place the order preference of candidate sites.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.183-194
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• 2014

Discussions for water resources vulnerability and index development with sustainable concept are actively being made in recent years. Based on such index, water resources vulnerability of present and future is determined and diagnosed. This study calculated the water resources vulnerability rankings by 152 nations, using indicator related to water resources assessment that can be obtained from World Bank, VRI (Vulnerability Resilience Indicator), ESI (Environmental Sustainability Index). In order to quantitatively assess of water resources vulnerability based on this indicator, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique was applied to index water vulnerability and to determine the rankings by nations. As a results, South Korea was ranked as the 88th among the 152 nations including Korea. Among the continents, Oceania was the least vulnerable and Afirica was the most vulnerable in continents. WUnited State, Japan, Korea and China were vulnerable in order among the major countries. Therefore, water resources vulnerability rankings by nations in this study helps us to better understand the situation of South Korea and provide the data for water resources planning and measure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.37-40
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• 2001

This paper describes the selection and evaluation of RP processes. Major rapid prototyping processes such as SLS, SLA, FDM and LOM, which are wide spread in use are selected. A test part, which includes various primitives, is designed in order to evaluate these RP processes. Measurement of the test part is automated by using a CMN program. To visualize and analyze measured data, Microsoft Access and Visual C++ are used. Also, from measured data obtained, TOPSIS, one of the decision making methods, and Shannon Entropy is used to select an appropriate RP process for specific application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3794-3820
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• 2019

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.10-19
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• 2013

Dispatching rule plays an important role in a hybrid flow shop. Finding the appropriate dispatching rule becomes more challenging when there are multiple criteria, uncertain demands, and dynamic manufacturing environment. Using a single dispatching rule for the whole shop or a set of rules based on a single criterion is not sufficient. Therefore, a multi-criteria decision making technique using 'the order preference by similarity to ideal solution' (TOPSIS) and 'analytic hierarchy process' (AHP) is presented. The proposed technique is aimed to find the most suitable set of dispatching rules under different manufacturing scenarios. A simulation based case study on a PCB manufacturing process is presented to illustrate the procedure and effectiveness of the proposed methodology.

• International Journal of Automotive Technology
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• v.8 no.1
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• pp.83-91
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• 2007

Nowadays, the increasing global competition forces manufacturing enterprises to apply new technologies such as laser welding to manufacturing of their products. In case of automotive industries, they interest in assembly system for BIW (Body in White) carrying out laser welding. In this paper, the method of implementation for digital laser welding assembly system is proposed. Based on the requirements of assembly tasks obtained through product analysis, process modeling is executed by using the IDEF0 and UML model. For digital assembly system, the selected components are modeled by using 3D CAD tools. According to the system configuration strategy, lots of the alternative solutions for the assembly system of welding side panels are generated. Finally, the optimal laser welding system is chosen by the evaluation of the alternative solutions with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.