R&D Project Selection with Gray-WASPAS Method

Main Article Content

Halil ŞEN
https://orcid.org/0000-0003-4062-5366

Abstract

Research and development (R&D) studies that carried out systematically to increase scientific and technical knowledge and to combine this knowledge with creativity and express its use in new applications, are extremely important in terms of sustainability in competition, development of new products and production processes, as well as the development and improvement of existing products and production systems. R&D has the same importance for cosmetics companies. Today, leading companies in the cosmetics industry allocate serious budgets to research and development activities to meet customer demands. Choosing the right R&D projects plays a key role in the correct use of this budget. This selection problem is a complex problem in terms of characteristics of alternatives, criteria and decision makers. In this study, the Gray-WASPAS (Gray - Weighted Aggregated Sum Product Assessment) method was chosen considering the characteristics of the criteria and the difficulties of expression in evaluating the alternatives according to these criteria, and this complex problem was solved.

Downloads

Download data is not yet available.

Article Details

How to Cite
ŞEN, H. (2023). R&D Project Selection with Gray-WASPAS Method. The European Journal of Research and Development, 3(1), 37–45. https://doi.org/10.56038/ejrnd.v3i1.224
Section
Articles

References

Poh, K. L., Ang, B. W., & Bai, F. (2001). A comparative analysis of R&D project evaluation methods. R and D Management, 31(1), 63–75. https://doi.org/10.1111/1467-9310.00197 DOI: https://doi.org/10.1111/1467-9310.00197

Meade, L. M., & Presley, A. (2002). R&D project selection using the analytic network process. IEEE Transactions on Engineering Management, 49(1), 59–66. https://doi.org/10.1109/17.985748 DOI: https://doi.org/10.1109/17.985748

Hsu, Y. G., Tzeng, G. H., & Shyu, J. Z. (2003). Fuzzy multiple criteria selection of government-sponsored frontier technology R&D projects. R&D Management, 33(5), 539–551. https://doi.org/10.1111/1467-9310.00315 DOI: https://doi.org/10.1111/1467-9310.00315

Wang, K., Wang, C. K., & Hu, C. (2005). Analytic hierarchy process with fuzzy scoring in evaluating multidisciplinary R&D projects in China. IEEE Transactions on Engineering Management, 52(1), 119–129. https://doi.org/10.1109/TEM.2004.839964 DOI: https://doi.org/10.1109/TEM.2004.839964

Mohantyy, R. P., Agarwalz, R., Choudhuryz, A. K., & Tiwari, M. K. (2005). A fuzzy ANP-based approach to R&D project selection: a case study. International Journal of Production Research, 43(24), 5199–5216. https://doi.org/10.1080/00207540500219031 DOI: https://doi.org/10.1080/00207540500219031

Linton, J. D., Morabito, J., & Yeomans, J. S. (2007). An extension to a DEA support system used for assessing R&D projects. R&D Management, 37(1), 29–36. https://doi.org/10.1111/J.1467-9310.2007.00456.X DOI: https://doi.org/10.1111/j.1467-9310.2007.00456.x

Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895–912. https://doi.org/10.1016/J.OMEGA.2006.05.002 DOI: https://doi.org/10.1016/j.omega.2006.05.002

Kuchta, D. (2007). A FUZZY MODEL FOR R&D PROJECT SELECTION WITH BENEFIT, OUTCOME AND RESOURCE INTERACTIONS. http://dx.doi.org/10.1080/00137910108967571, 46(3), 164–180. https://doi.org/10.1080/00137910108967571 DOI: https://doi.org/10.1080/00137910108967571

Cheung, M. T., Greenfield, P. F., & Liao, Z. (2009). Selecting R&D projects for technology-based innovation: An application of the core model approach. IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management, 974–977. https://doi.org/10.1109/IEEM.2009.5373527 DOI: https://doi.org/10.1109/IEEM.2009.5373527

Wang, Z., & Yu, Y. (2011). Information entropy method for project portfolio selection. Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 4, 2618–2622. https://doi.org/10.1109/FSKD.2011.6020005 DOI: https://doi.org/10.1109/FSKD.2011.6020005

Liu, S. S., & Wang, C. J. (2011). Optimizing project selection and scheduling problems with time-dependent resource constraints. Automation in Construction, 20(8), 1110–1119. https://doi.org/10.1016/J.AUTCON.2011.04.012 DOI: https://doi.org/10.1016/j.autcon.2011.04.012

Karasakal, E., & Aker, P. (2017). A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem. Omega (United Kingdom), 73, 79–92. https://doi.org/10.1016/J.OMEGA.2016.12.006 DOI: https://doi.org/10.1016/j.omega.2016.12.006

Kundakcı, N., & Kas Bayrakdaroğlu, F. (2019). BULANIK EDAS YÖNTEMİ İLE AR-GE PROJESİ SEÇİMİ. Uluslararası İktisadi ve İdari İncelemeler Dergisi. https://doi.org/10.18092/ulikidince.538332 DOI: https://doi.org/10.18092/ulikidince.538332

Binici, E., & Aksakal, E. (2020). A new approach to R-D project selection problem and a solution proposal: UTA method. Pamukkale University Journal of Engineering Sciences, 26(1), 211–226. https://doi.org/10.5505/pajes.2019.45945 DOI: https://doi.org/10.5505/pajes.2019.45945

Ju-Long, D. (1982). Control problems of grey systems. Systems and Control Letters, 1(5), 288–294. https://doi.org/10.1016/S0167-6911(82)80025-X DOI: https://doi.org/10.1016/S0167-6911(82)80025-X

Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/J01.EEE.122.6.1810 DOI: https://doi.org/10.5755/j01.eee.122.6.1810

Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2015). Selecting a Contractor by Using a Novel Method forMultiple Attribute Analysis: Weighted Aggregated SumProduct Assessment with Grey Values (WASPAS-G). Studies in Informatics and Control, 24(2), 141–150. https://doi.org/10.24846/V24I2Y201502 DOI: https://doi.org/10.24846/v24i2y201502

Pavlovskis, M., & Antuchevičienė, J. (2016). Importance and Advantages of Conversion of Unused Industrial Buildings in Lithuania. Mokslas - Lietuvos Ateitis, 8(5), 475–483. https://doi.org/10.3846/MLA.2016.971 DOI: https://doi.org/10.3846/mla.2016.971

Leonavičiute, G., Dejus, T., & Antuchevičiene, J. (2016). Analysis and prevention of construction site accidents. Građevinar, 68(5), 399–410. https://doi.org/10.14256/JCE.1428.2015 DOI: https://doi.org/10.14256/JCE.1428.2015

Pavlovskis, M., Antucheviciene, J., & Migilinskas, D. (2016). Application of MCDM and BIM for Evaluation of Asset Redevelopment Solutions. Studies in Informatics and Control, 25(3), 293–302. https://doi.org/10.24846/V25I3Y201603 DOI: https://doi.org/10.24846/v25i3y201603

Bakhat, R., & Rajaa, M. (2019). Developing a novel Grey integrated multi-criteria approach for enhancing the supplier selection procedure: A real-world case of Textile Company. undefined, 8(3), 211–224. https://doi.org/10.5267/J.DSL.2019.4.001 DOI: https://doi.org/10.5267/j.dsl.2019.4.001

Wang, C. N., Kao, J. C., Wang, Y. H., Nguyen, V. T., Nguyen, V. T., & Husain, S. T. (2021). A Multicriteria Decision-Making Model for the Selection of Suitable Renewable Energy Sources. Mathematics, 9(12). https://doi.org/10.3390/MATH9121318 DOI: https://doi.org/10.3390/math9121318