PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection
Abstract
With the rapid development of 3D printing technology, 3D printers are manufactured based on the principle of 3D printing technology are more and more widely used in the manufacturing industry. Choosing high quality 3D printers for industrial production is of great significance to the economic growth of enterprises. In fact, it is difficult to select the most optimal 3D printers under a single and simple standard. Therefore, this paper establishes the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for the multiple attribute group decision making (MAGDM). Then the CRITIC model is introduced to derive objective weight and the cumulative prospect theory is leaded into obtain the cumulative weight of PDHLTS. In addition, what’s more, the PDHL-EDAS method is built and applied to the choice of high-quality 3D printer. Finally, compared with the available MAGDM methods under PDHLTS, the built method is proved to be scientific and effective.
First published online 15 December 2021
Keyword : multiple attribute group decision making (MAGDM), probabilistic double hierarchy linguistic term set (PDHLTS), EDAS method, CRITIC method, 3D printer selection
How to Cite
Lei, F., Wei, G., Shen, W., & Guo, Y. (2022). PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection. Technological and Economic Development of Economy, 28(1), 179–200. https://doi.org/10.3846/tede.2021.15884
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Cheng, Y. L., Qin, H. T., Acevedo, N. C., Jiang, X. P., & Shi, X. L. (2020). 3D printing of extended-release tablets of theophylline using hydrox-ypropyl methylcellulose (HPMC) hydrogels. International Journal of Pharmaceutics, 591, 11983. https://doi.org/10.1016/j.ijpharm.2020.119983
Chrispin, T. T. B., Takano, C. C., Fernandes, M. S., & Sartori, M. G. F. (2020). Development of vaginal devices by 3D printer for gynecological use. International Urogynecology Journal, 31, S52–S53.
Darko, A. P., & Liang, D. C. (2020). Some q-rung orthopair fuzzy Hamacher aggregation operators and their application to multiple attribute group decision making with modified EDAS method. Engineering Applications of Artificial Intelligence, 87, 103259. https://doi.org/10.1016/j.engappai.2019.103259
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Comput-ers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Feng, X. Q., Wei, C. P., & Liu, Q. (2018). EDAS method for extended hesitant fuzzy linguistic multi-criteria decision making. International Jour-nal of Fuzzy Systems, 20, 2470–2483. https://doi.org/10.1007/s40815-018-0504-5
Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. J. I. (2015). Multi-criteria inventory classification using a new method of Evaluation based on Distance from Average Solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Gou, X., Liao, H., Xu, Z., & Herrera, F. (2017). Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures. Information Fusion, 38, 22–34. https://doi.org/10.1016/j.inffus.2017.02.008
Gou, X., Xu, Z., Liao, H., & Herrera, F. (2021). Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: The application in smart healthcare. Journal of the Operational Research Society, 72(12), 2611–2630. https://doi.org/10.1080/01605682.2020.1806741
Gundogdu, F. K., Kahraman, C., & Civan, H. N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent & Fuzzy Systems, 35(6), 6353–6365. https://doi.org/10.3233/JIFS-181172
He, Y., Lei, F., Wei, G. W., Wang, R., Wu, J., & Wei, C. (2019). EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection. International Journal of Computational Intelligence Systems, 12(3), 1361–1370. https://doi.org/10.2991/ijcis.d.191028.001
Karunanithi, K., Han, C., Lee, C. J., Shi, W. C., Duan, L., & Qian, Y. (2015). Identification of a hemodynamic parameter for assessing treatment outcome of EDAS in Moyamoya disease. Journal of Biomechanics, 48(2), 304–309. https://doi.org/10.1016/j.jbiomech.2014.11.029
Kundakci, N. (2019). An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives. Journal of Mul-ti-Criteria Decision Analysis, 26(1–2), 27–34. https://doi.org/10.1002/mcda.1656
Lei, F., Wei, G., & Chen, X. (2021). Model-based evaluation for online shopping platform with probabilistic double hierarchy linguistic CODAS method. International Journal of Intelligent Systems, 36(9), 5339–5358. https://doi.org/10.1002/int.22514
Liang, W. Z., Zhao, G. Y., & Luo, S. Z. (2018). An integrated EDAS-ELECTRE method with picture fuzzy information for cleaner production evaluation in gold mines. IEEE Access, 6, 65747–65759. https://doi.org/10.1109/ACCESS.2018.2878747
Liu, Y. S., Zhang, R., Ye, H. Q., Wang, S. M., Wang, K. P., Liu, Y. S., & Zhou, Y. S. (2019). The development of a 3D colour reproduction sys-tem of digital impressions with an intraoral scanner and a 3D printer: A preliminary study. Scientific Reports, 9, 20052. https://doi.org/10.1038/s41598-019-56624-3
Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Pavan, L. I., Bourguignon, G. A., Soderini, H., & Ubertazzi, E. P. (2021). Vaginoplasty: Modificated McIndoe using xenograft and a tailored 3D-printer mold. International Urogynecology Journal, 32, 2283–2285. https://doi.org/10.1007/s00192-021-04689-y
Prabhu, S. R., & Ilangkumaran, M. (2019a). Decision making methodology for the selection of 3D printer under fuzzy environment. International Journal of Materials & Product Technology, 59(3), 239–252. https://doi.org/10.1504/IJMPT.2019.102935
Prabhu, S. R., & Ilangkumaran, M. (2019b). Selection of 3D printer based on FAHP integrated with GRA-TOPSIS. International Journal of Ma-terials & Product Technology, 58(2/3), 155–177. https://doi.org/10.1504/IJMPT.2019.097667
Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., & Turskis, Z. (2017). An extension of the EDAS method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5–12. https://doi.org/10.24846/v26i1y201701
Wang, P., Wang, J., & Wei, G. W. (2019). EDAS method for multiple criteria group decision making under 2-tuple linguistic neutrosophic envi-ronment. Journal of Intelligent & Fuzzy Systems, 37(2), 1597–1608. https://doi.org/10.3233/JIFS-179223
Wei, C., Wu, J., Guo, Y., & Wei, G. (2021a). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078
Wei, G., Lin, R., Lu, J., Wu, J., & Wei, C. (2021b). The generalized dice similarity measures for probabilistic uncertain linguistic MAGDM and its application to location planning of electric vehicle charging stations. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-021-01084-z
Wei, G., Wei, C., & Guo, Y. (2021c). EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection. Soft Computing, 25, 9045–9053. https://doi.org/10.1007/s00500-021-05842-x
Yi, J. H., Duling, M. G., Bowers, L. N., Knepp, A. K., LeBouf, R. F., Nurkiewicz, T. R., Ranpara, A., Luxton, T., Martin, S. B., Burns, D. A., Peloquin, D. M., Baumann, E. J., Virji, M. A., & Stefaniak, A. B. (2019). Particle and organic vapor emissions from children’s 3-D pen and 3-D printer toys. Inhalation Toxicology, 31(13–14), 432–445. https://doi.org/10.1080/08958378.2019.1705441
Chrispin, T. T. B., Takano, C. C., Fernandes, M. S., & Sartori, M. G. F. (2020). Development of vaginal devices by 3D printer for gynecological use. International Urogynecology Journal, 31, S52–S53.
Darko, A. P., & Liang, D. C. (2020). Some q-rung orthopair fuzzy Hamacher aggregation operators and their application to multiple attribute group decision making with modified EDAS method. Engineering Applications of Artificial Intelligence, 87, 103259. https://doi.org/10.1016/j.engappai.2019.103259
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Comput-ers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
Feng, X. Q., Wei, C. P., & Liu, Q. (2018). EDAS method for extended hesitant fuzzy linguistic multi-criteria decision making. International Jour-nal of Fuzzy Systems, 20, 2470–2483. https://doi.org/10.1007/s40815-018-0504-5
Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. J. I. (2015). Multi-criteria inventory classification using a new method of Evaluation based on Distance from Average Solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Gou, X., Liao, H., Xu, Z., & Herrera, F. (2017). Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures. Information Fusion, 38, 22–34. https://doi.org/10.1016/j.inffus.2017.02.008
Gou, X., Xu, Z., Liao, H., & Herrera, F. (2021). Probabilistic double hierarchy linguistic term set and its use in designing an improved VIKOR method: The application in smart healthcare. Journal of the Operational Research Society, 72(12), 2611–2630. https://doi.org/10.1080/01605682.2020.1806741
Gundogdu, F. K., Kahraman, C., & Civan, H. N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent & Fuzzy Systems, 35(6), 6353–6365. https://doi.org/10.3233/JIFS-181172
He, Y., Lei, F., Wei, G. W., Wang, R., Wu, J., & Wei, C. (2019). EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection. International Journal of Computational Intelligence Systems, 12(3), 1361–1370. https://doi.org/10.2991/ijcis.d.191028.001
Karunanithi, K., Han, C., Lee, C. J., Shi, W. C., Duan, L., & Qian, Y. (2015). Identification of a hemodynamic parameter for assessing treatment outcome of EDAS in Moyamoya disease. Journal of Biomechanics, 48(2), 304–309. https://doi.org/10.1016/j.jbiomech.2014.11.029
Kundakci, N. (2019). An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives. Journal of Mul-ti-Criteria Decision Analysis, 26(1–2), 27–34. https://doi.org/10.1002/mcda.1656
Lei, F., Wei, G., & Chen, X. (2021). Model-based evaluation for online shopping platform with probabilistic double hierarchy linguistic CODAS method. International Journal of Intelligent Systems, 36(9), 5339–5358. https://doi.org/10.1002/int.22514
Liang, W. Z., Zhao, G. Y., & Luo, S. Z. (2018). An integrated EDAS-ELECTRE method with picture fuzzy information for cleaner production evaluation in gold mines. IEEE Access, 6, 65747–65759. https://doi.org/10.1109/ACCESS.2018.2878747
Liu, Y. S., Zhang, R., Ye, H. Q., Wang, S. M., Wang, K. P., Liu, Y. S., & Zhou, Y. S. (2019). The development of a 3D colour reproduction sys-tem of digital impressions with an intraoral scanner and a 3D printer: A preliminary study. Scientific Reports, 9, 20052. https://doi.org/10.1038/s41598-019-56624-3
Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Pavan, L. I., Bourguignon, G. A., Soderini, H., & Ubertazzi, E. P. (2021). Vaginoplasty: Modificated McIndoe using xenograft and a tailored 3D-printer mold. International Urogynecology Journal, 32, 2283–2285. https://doi.org/10.1007/s00192-021-04689-y
Prabhu, S. R., & Ilangkumaran, M. (2019a). Decision making methodology for the selection of 3D printer under fuzzy environment. International Journal of Materials & Product Technology, 59(3), 239–252. https://doi.org/10.1504/IJMPT.2019.102935
Prabhu, S. R., & Ilangkumaran, M. (2019b). Selection of 3D printer based on FAHP integrated with GRA-TOPSIS. International Journal of Ma-terials & Product Technology, 58(2/3), 155–177. https://doi.org/10.1504/IJMPT.2019.097667
Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., & Turskis, Z. (2017). An extension of the EDAS method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5–12. https://doi.org/10.24846/v26i1y201701
Wang, P., Wang, J., & Wei, G. W. (2019). EDAS method for multiple criteria group decision making under 2-tuple linguistic neutrosophic envi-ronment. Journal of Intelligent & Fuzzy Systems, 37(2), 1597–1608. https://doi.org/10.3233/JIFS-179223
Wei, C., Wu, J., Guo, Y., & Wei, G. (2021a). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078
Wei, G., Lin, R., Lu, J., Wu, J., & Wei, C. (2021b). The generalized dice similarity measures for probabilistic uncertain linguistic MAGDM and its application to location planning of electric vehicle charging stations. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-021-01084-z
Wei, G., Wei, C., & Guo, Y. (2021c). EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection. Soft Computing, 25, 9045–9053. https://doi.org/10.1007/s00500-021-05842-x
Yi, J. H., Duling, M. G., Bowers, L. N., Knepp, A. K., LeBouf, R. F., Nurkiewicz, T. R., Ranpara, A., Luxton, T., Martin, S. B., Burns, D. A., Peloquin, D. M., Baumann, E. J., Virji, M. A., & Stefaniak, A. B. (2019). Particle and organic vapor emissions from children’s 3-D pen and 3-D printer toys. Inhalation Toxicology, 31(13–14), 432–445. https://doi.org/10.1080/08958378.2019.1705441