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A Hybrid Method based on BWM and TOPSIS-LP Model to Assess Computer Numerical Control Machines |
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| รหัสดีโอไอ | |
| Creator | Prasit Kailomsom |
| Title | A Hybrid Method based on BWM and TOPSIS-LP Model to Assess Computer Numerical Control Machines |
| Contributor | Pariwat Nasawat, Wanrop Khunthirat, Wasana Phuangpornpitak |
| Publisher | Faculty of Engineering Mahasasakham University |
| Publication Year | 2568 |
| Journal Title | Engineering Access |
| Journal Vol. | 11 |
| Journal No. | 1 |
| Page no. | 108-118 |
| Keyword | Best-worst method, multi-attribute decision-making,, TOPSIS, TOPSIS linear programming model, Best-Worst Method, Multi-Attribute Decision-Making, TOPSIS, TOPSIS linear programming model, Computer Numerical Control machines |
| URL Website | https://ph02.tci-thaijo.org/index.php/mijet/index |
| Website title | THAIJO Engineering Access |
| ISSN | 2730-4175 |
| Abstract | The escalating global competitiveness within the industrial sector has necessitated a critical requirement to enhance facilities to meet market demands. In this context, the selection of computer numerical control (CNC) machines plays a vital role in enhancing productivity and manufacturing flexibility. This study presents a hybrid method combining the Best-Worst Method (BWM) with a novel TOPSIS linear programming (TOPSIS-LP) model for CNC machine selection. The BWM is employed to address information challenges and simplify data collection by assigning weights to specified criteria. These weights are then utilized in the TOPSIS-LP model to rank alternatives based on their closeness coefficients. A numerical illustration is conducted to validate the proposed model, with outcomes compared to established Multi-Attribute Decision-Making (MADM) methodologies. The Spearman correlation matrix reveals strong correlations between the proposed method and existing methods, such as MOORA (0.96) and WASPAS (0.88), highlighting the hybrid approach's reliability. This method provides a straightforward and reliable tool for organizations to make informed CNC machine selections, ultimately improving their productivity and manufacturing capabilities. |
