ارزیابی، رتبه‌بندی و انتخاب موثرترین طرح‌های پژوهشی در دانشگاه‌ها براساس سیاست‌های پژوهش و فناوری (مطالعه‌ی موردی: دانشگاه صنعتی شیراز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیئت‌علمی، دانشکده مهندسی صنایع، دانشگاه صنعتی شیراز، شیراز.

2 دانشجوی کارشناسی‌ارشد مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه صنعتی شیراز، شیراز.

چکیده

قانون برنامه پنج‌ساله ششم توسعه اقتصادی، اجتماعی و فرهنگی کشور (1400-1396)، سیاست‌ها و اولویت‌های پژوهش‌ و ‌فناوری کشور (1400-1396) و طرح تحول همکاری‌های دانشگاه‌ و صنعت (مصوب وزارت علوم، تحقیقات و فناوری) بر انجام فعالیت‌های تحقیق ‌و ‌توسعه مبتنی بر نیازهای جامعه‌ و ‌صنعت در‌ قالب طرح‌های پژوهشی تأکید دارند. براین‌اساس این پژوهش رویکردی را جهت ارزیابی و رتبه‌بندی طرح‌های پژوهشی فناوری‌محور در چهار مرحله پیشنهاد می‌دهد. در مرحله اول معیارهای کلیدی در ارزیابی طرح‌های پژوهشی شناسایی می‌شوند. در مرحله دوم ابتدا با فن دیمتل وابستگی‌های متقابل معیارها مشخص‌شده و سپس با فرآیند تحلیل شبکه اهمیت نسبی آن‌ها تعیین می‌شود. در مرحله سوم، رتبه‌ی پروژه‌ها مشخص می‌شود و در مرحله چهارم ضمن مقایسه نتایج و تحلیل حساسیت نسبت به معیارها تحت سیزده سناریو، با روش تخصیص خطی رتبه نهایی طرح‌های پژوهشی تعیین می‌شود. بر­اساس چارچوب مذکور، در این مطالعه 23 طرح پژوهشی فناوری‌محور در دانشگاه صنعتی شیراز در هفت بُعد مشتمل بر 19 معیار ارزیابی و رتبه‌بندی شده است. نتایج پژوهش مبین اولویت ابعاد مالی، بازاریابی و فنی در انتخاب پروژه‌های تحقیق و توسعه می‌باشد. به‌کارگیری یک روش نظام‌مند در انتخاب طرح‌های پژوهشی در دانشگاه‌ها و مراکز پژوهشی کشور، امکان تعریف آن‌ها را با دقت بیشتری فراهم می‌نماید.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation, Ranking and Selection of the Most Effective Research Projects in Universities Based on the Research and Technology Policies (Case study: Shiraz University of Technology)

نویسندگان [English]

  • Mahmood Eghtesadifard 1
  • Fatemeh Shokoohi 2
1 Faculty Member, Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran.
2 MSc. student in Industrial Engineering, Shiraz University of Technology, Shiraz, Iran.
چکیده [English]

The Sixth Five-Year Economic, Cultural, and Social Development Plan of Iran (2016-2021), the research and technology priorities and policies of the country (2016-2021) and the Transformation Plan of Cooperation between Universities and Industry (approved by MSRT) all emphasize the importance of the R&D activities based on the demands of industry and society in the form of research projects. This study proposes an approach including four stages to evaluate and rank technology-oriented research projects. In the first stage, the key criteria in evaluating research projects are identified. In the second stage, the interdependencies of the criteria are identified by DEMATEL, and then by the analytic network process, the relative importance of the criteria is determined. In the third stage, the rank of the projects is determined and in the fourth stage, the results are compared and then sensitivity analysis of the criteria is carried out under thirteen scenarios. Finally, the results are integrated by the linear assignment method and the final rank is determined. In this study, 23 projects at the Shiraz University of Technology have been evaluated in seven dimensions including 19 criteria and then they have been ranked. The research results indicate the high priority of financial, marketing and technical dimensions in the selection of R & D projects. Thus, using a systematic approach in selecting projects allows defining them with greater accuracy.

کلیدواژه‌ها [English]

  • Evaluating and ranking of R&D projects
  • Multi-criteria decision making
  • DEMATEL
  • Analytic network process
  • Linear assignment method
-          Bakshi, T., Sinharay, A., Sarkar, B., & kumar Sanyal, S. (2011, December). MCDM based project selection by F-AHP & VIKOR. In Panigrahi B.K., Suganthan P.N., Das S., Satapathy S.C. (Eds.), Swarm, Evolutionary, and Memetic Computing (pp. 381-388). Springer. https://doi.org/10.1007/978-3-642-27172-4_47
-          Bhattacharyya, R. (2015). A grey theory based multiple attribute approach for R&D project portfolio selection. Fuzzy Information and Engineering, 7(2), 211-225. https://doi.org/10.1016/j.fiae.2015.05.006
-          Bolat, B., Çebi, F., Tekin Temur, G., & Otay, I. (2014). A fuzzy integrated approach for project selection. Journal of Enterprise Information Management, 27(3), 247-260. https://doi.org/10.1108/JEIM-12-2013-0091
-          Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445-469. http://control.ibspan.waw.pl:3000/contents/export?filename=2006-2-12_brauers_et_al.pdf
-          Cheng, C. H., Liou, J., & Chiu, C. Y. (2017). A consistent fuzzy preference relations based ANP model for R&D project selection. Sustainability, 9(8), 1352. https://doi.org/10.3390/su9081352
-          Chiang, T. A., & Che, Z. H. (2010). A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA. Expert Systems with Applications, 37(11), 7408-7418. https://doi.org/10.1016/j.eswa.2010.04.034
-          Chiu, W. Y., Tzeng, G. H., & Li, H. L. (2013). A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowledge-Based Systems, 37, 48-61. https://doi.org/10.1016/j.knosys.2012.06.017
-          Eghtesadifard, M., Afkhami, P., & Bazyar, A. (2020). An integrated approach to the selection of municipal solid waste landfills through GIS, K-Means and multi-criteria decision analysis. Environmental Research, 109348. https://doi.org/10.1016/j.envres.2020.109348
-          Feng, B., Ma, J., & Fan, Z. P. (2011). An integrated method for collaborative R&D project selection: Supporting innovative research teams. Expert Systems with Applications, 38(5), 5532-5543. https://doi.org/10.1016/j.eswa.2010.10.083
-          Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, 1–8.
-          Ghorabaee, M. K., Amiri, M., Sadaghiani, J. S., & Zavadskas, E. K. (2015). Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets. International Journal of Information Technology & Decision Making, 14(05), 993-1016. https://doi.org/10.1142/S0219622015500212
-          Grady, C. A., He, X., & Peeta, S. (2015). Integrating social network analysis with analytic network process for international development project selection. Expert Systems with Applications, 42(12), 5128-5138. https://doi.org/10.1016/j.eswa.2015.02.039
-          Gür, Ş., Hamurcu, M., & Eren, T. (2016). Using analytic network process and goal programming methods for project selection in the public institution. Les Cahiers du MECAS, 13, 36-51. https://www.asjp.cerist.dz/en/article/8733
-          Huang, C. C., Chu, P. Y., & Chiang, Y. H. (2008). A fuzzy AHP application in government-sponsored R&D project selection. Omega, 36(6), 1038-1052. https://doi.org/10.1016/j.omega.2006.05.003
-          Jung, U., & Seo, D. (2010). An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria. Decision Support Systems, 49(3), 335-342. https://doi.org/10.1016/j.dss.2010.04.005
-          Karasakal, E., & Aker, P. (2017). A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem. Omega, 73, 79-92. https://doi.org/10.1016/j.omega.2016.12.006
-          Liu, F., Chen, Y .W., Yang, J. B., Xu, D. L., & Liu, W. (2019). Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule. International Journal of Project Management, 37(1), 87-97. https://doi.org/10.1016/j.ijproman.2018.10.006
-          Mohanty, R. P. (1992). Project selection by a multiple-criteria decision-making method: An example from a developing country. International Journal of Project Management, 10(1), 31-38. https://doi.org/10.1016/0263-7863(92)90070-P
-          Mohanty, R. P., Agarwal, R., Choudhury, 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
-          Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). RWS Publ.
-          Tavana, M., Keramatpour, M., Santos-Arteaga, F. J., & Ghorbaniane, E. (2015). A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert Systems with Applications, 42(22), 8432-8444. https://doi.org/10.1016/j.eswa.2015.06.057
-          Tavana, M., Yazdani, M., & Di Caprio, D. (2017). An application of an integrated ANP–QFD framework for sustainable supplier selection. International Journal of Logistics Research and Applications, 20(3), 254-275. https://doi.org/10.1080/13675567.2016.1219702
-          Tuzkaya, U. R., & Yolver, E. (2015). R&D project selection by integrated grey analytic network process and grey relational analysis: an implementation for home appliances company. Journal of Aeronautics and Space Technologies, 8(2), 35-41. https://doi.org/10.7603/s40690-015-0014-8
-          Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multicriteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3), 131-139.
-          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