Optimization of the Sizing Process with Grey Relational Analysis

Sarpkaya, Çiğdem; Sabir, Emel Ceyhun

doi:10.5604/12303666.1172087

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…These data are the starting step for applying GRA. To apply the GRA method we respect the methodological steps and algorithm according to the international literature (Lusi et al, 2020;Nayakappa et al, 2019;Xu et al, 2014;Gerus -Gosciewska & Gosciewski, 2022;Wu & Zhan, 2017;Skrinjaric, 2021;Lusi et al, 2020;Ertugrul et al, 2018;Badi and Elghoul, 2023;Sarpkaya & Sabir, 2016;Fidan, 2020;Girish et al, 2019;Krstić et al, 2023) as follows (Figure 1) and mentioned in Appendix A.…”

Section: Methodsmentioning

confidence: 99%

“…To validate the results of GRA as a method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during OCVID-19 pandemic time) we applied a GLM -General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons (for observed means) take into consideration that according to the scientific literature regarding the GRA method, in different scientific fields, a combination of GRA and ANOVA are usually applied known as Taguchi -GRA (Sarpkaya & Sabir, 2016;JAER, 2020). Therefore, starting with the new ranks resulted from GRA and considering as dependent variable of the GLM model the rank of TTCI 2021-during pandemic, the independent variables of the model were considered: quartile GRA rank of TTCI 2019, quartile of GRA rank for overnight stays, interaction effects of quartile GRA rank of TTCI 2019 * quartile of GRA rank for overnight stays.…”

Section: Select Optimum Level Based On Grade Valuementioning

confidence: 99%

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Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Gabor,

Curta,

Oltean

2024

Economics

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The tourism country’s competitiveness is important especially when countries strive for bigger market shares as European countries are. The COVID-19 pandemic has had devastating effects on tourism and the entire tourism industry must be rethinking and reshaping given some opportunities and challenges to the entrepreneurs, local communities, local administrations, governments of competitiveness destinations. In this paper we measured if the Google and Apple mobility indices cand predict (or not) the movement of TTCI during pandemic compared with pre-pandemic TTCI ranks by using grey system theory and statistical methods. The aim of the paper is to validate the Grey Relational Analysis (GRA) as identifier of the good predictor (statistically significant for p-value< 0.05) for European tourism competitiveness (measured by TTCI) in particular conditions, especially before and during COVID-19 pandemic time by take into consideration the Google Mobility and Apple Mobility data and their relationship with overnight stays for 11 European countries. To validate the GRA as method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during COVID-19 pandemic time) a GLM – General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons was applied. Our results validated the powerful of GRA for tourism competitiveness, statistically significant according to GLM with interaction effects, and emphasis (especially for entrepreneurs) that the overnight stays will give the right rank on top of tourism competitiveness, prior to other ITC support as Google and Apple Mobility indices proved by reflection of overnights on TTCI 2021 – during pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Select Optimum Level Based On Grade Valuementioning

confidence: 99%

Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Gabor,

Curta,

Oltean

2024

Economics

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“…GRA is a highly valuable tool for conducting multi-response data analysis. It allows for the simultaneous optimization of Taguchi experimental designs with multiple responses [3,[7][8][9][10][11][12][23][24][25][26][27]. The core concept is centered on using GRA to optimize these multiple responses by leveraging S/N Ratio values obtained with Taguchi method, ensuring they are in line with desired characteristics.…”

Section: Signed To Targetmentioning

confidence: 99%

“…Several research [1][2][3][4][5] have used Taguchi and Grey Relational Analysis (GRA) methods to optimize sizing parameters for good strength of sized yarn. Other studies [6][7][8][9][10], conducted research that focused on optimizing the process condition.…”

Section: Introductionmentioning

confidence: 99%

Redesign of the sizing process optimization to produce warp yarn with strong weaving ability

Kartika,

Soepardi

2023

Jurnal Optimasi Sistem Industri

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The research focuses on optimization of the sizing process parameters for cotton warp yarn material. The sizing process is a crucial step where warp yarn is coated with a starch solution before weaving. The aim is to produce warp yarn with strong weaving ability, thereby facilitating the entire process. To achieve this, several factors are considered, namely the steam bar, and squeeze roll pressure, including machine speed. The techniques used to address this issue include Taguchi in collaboration with Grey Relational Analysis (GRA) to effectively address multi-responses. In this analysis, the larger the better characteristic was applied to S/N Ratio, indicating that the higher the GRG value, the closer it is to the desired optimum condition. Based on the calculations from S/N Ratio response table for the GRG, it can be determined that factor steam bar pressure was selected at 4 Bar, with an average S/N Ratio value of -2.935. factor squeeze roll should be set to level 10 Km/m3, as it yields an average S/N Ratio value of -2.912. Factors squeeze roll) and C (Machine Speed) were selected at levels 2 (10 Km/m3) and 1 (45 M/Min) with average values of -2.912, and -4.771, respectively.

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Analysis of Multi Objective Response Variables for the Milling Characteristics of Al-SiC Particulate Composites Using Taguchi Based Grey Relational Analysis

Kavitha

Gladson²

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Optimization of the Sizing Process with Grey Relational Analysis

Cited by 9 publications

References 12 publications

Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Redesign of the sizing process optimization to produce warp yarn with strong weaving ability

Analysis of Multi Objective Response Variables for the Milling Characteristics of Al-SiC Particulate Composites Using Taguchi Based Grey Relational Analysis

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