SELECTION AMONG INNOVATIVE PROJECT PROPOSALS USING A HESITANT FUZZY MULTIPLE CRITERIA DECISION MAKING METHOD

Year 2017, Volume: 4 Issue: 2, 194 - 202, 30.06.2017

Basar Oztaysi , Sezi Cevik Onar , Cengiz Kahraman

https://doi.org/10.17261/Pressacademia.2017.449

Abstract

Purpose- In recent decades, innovation and desearch and development (R&D) has
been the key component for growth and economic competitiveness for companies
and countries.

Methodology- Since innovation Project require considerable funds and contain risk, it
is important to evaluate their potantial performance and return on investment
to make proper decision.

Findings- The objective of this study is to develop a decision model for
innovative Project selection using multicriteria decision making model (MCDM)
and Hesitant Fuzzy sets. By using MCDM approach, various perspectives on
project evaluation can be integrated into decision making model.

Conclusion-
Employing hesitant fuzzy sets enable a better
representation of decision makers’ inguistic evaluations and thus provide
better results. 

Keywords

Innovative projects, hesitant fuzzy sets, multiple criteria, decision making

References

• Meuer, J., Rupietta, C., Backes-Gellner, U. 2015. “Layers of co-existing innovation systems”. Research Policy, 44(4), 888-910.
• Gourville, J. 2006, “Eager seller and stony buyers: understanding the psychology of new-product adaptation”. Harvard Business Review, 84(6), 98-106.
• Mohagheghi V., Mousavi S.M., Siadat A. 2015, "A new approach in considering vagueness and lack of knowledge for selecting sustainable portfolio of production projects" In: IEEE conference on industrial engineering and engineering management (IEEM), pp 1732–1736.
• Heydari, T., Seyed Hosseini, S.-M., Makui, A. 2016, “Developing and solving an one-zero non-linear goal programming model to R and D portfolio project selection with interactions between projects”, International Business Management, 10(19), pp. 4516-4521.
• OECD, 2015. The OECD Innovation Strategy – 2015, http://www.oecd.org/innovation/innovation-imperative.htm, last accessed: 02.03.2017.
• Heidenberger K. Stummer C. 1999, “Research and development project selection and resource allocation: a review of quantitative modelling approaches”, International Jurnal of Management Reviews, Volume 1, Issue 2, Pages 197–224.
• Oztaysi, B., Onar, S.C., Goztepe, K., Kahraman C. 2017 “Evaluation of research proposals for grant funding using interval-valued intuitionistic fuzzy sets”, Soft Computing, Volume 21, Issue 5, pp 1203–1218.
• Morton A., Keiser J.M., Salo A. 2016. “Multicriteria Portfolio Decision Analysis for Project Selection, Multiple Criteria Decision Analysis”, Volume 233 of the series International Series in Operations Research & Management Science pp 1269-1298.
• Read L., Madani K., Mokhtari S., Hanks C. 2017, “Stakeholder-driven multi-attribute analysis for energy project selection under uncertainty”, Energy, Volume 119, Pages 744-753.
• Rouhani S.2017 “A fuzzy superiority and inferiority ranking based approach for IT service management software selection”, Kybernetes, Vol. 46 Issue: 4, pp.728-746, (2017).
• Oztaysi B., Cevik Onar B., Kahraman C. 2016 “Fuzzy multicriteria prioritization of Urban transformation projects for Istanbul”, Journal of Int. & Fuzzy Systems 30 (4), 2459-2474i
• Stojcetovic B., Nikolic D., Velinov V., Bogdanovic D. 2016, “Application of integrated strengths, weaknesses, opportunities, and threats and analytic hierarchy process methodology to renewable energy project selection in Serbia”, Journal of Renewable and Sustainable Energy, 8.
• Oztaysi, B. 2015. “A Group Decision Making Approach Using Interval Type-2 Fuzzy AHP for Enterprise Information Systems Project Selection”, Journal of Multiple-Valued Logic and Soft Computing, Volume: 24 Issue: 5-6 Pages: 475-500.
• Mousavi, S. M., Vahdani, B., Hashemi, H., Ebrahimnejad, S. 2015. “An Artificial Intelligence Model-Based Locally Linear Neuro-Fuzzy for Construction Project Selection”, Journal of Multiple-Valued Logic & Soft Computing, Vol. 25 Issue 6, p589-604.
• Zadeh L.A. 1965, “Fuzzy sets”, Information and Control 8 (3) 338–353. Torra V. 2010, “Hesitant fuzzy sets”, Inernational Journal of Intelligent Systems, Volume 25, Issue 6, Pages 529–539.
• Liu, F., Zhu, W.D., Chen, Y.W., Xu, D.L., Yang, J.B. 2017, “Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach”, Scientometrics, pp. 1-19.
• Çağlar, M., Gürel, S. 2017, “Public R&D project portfolio selection problem with cancellations”, OR Spectrum,pp. 1-29
• Karaveg, C., Thawesaengskulthai, N., Chandrachai, A. 2016, “R&D commercialization capability criteria: implications for project selection”, Journal of Management Development 35(3), pp. 304-325.
• Mohagheghi, V., Mousavi, S.M., Vahdani, B., Shahriari, M.R. 2016, “R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach”, Neural Computing and Applications, pp. 1-20.
• Heydari, T., Seyed Hosseini, S.M., Makui, A., 2016, “Developing and solving an one-zero non-linear goal programming model to R and D portfolio project selection with interactions between projects”, International Business Management, 10(19), pp. 4516-4521.
• Cluzel, F., Yannou, B., Millet, D., Leroy, Y. 2016. “Eco-ideation and eco-selection of R&D projects portfolio in complex systems industries”, Journal of Cleaner Production, 112, pp. 4329-4343.
• Zhao, X., Yang, Y., Wu, G., Yang, J., Xue, X., 2012. “A dynamic and fuzzy modeling approach for multi-objective R & D project portfolio selection”, Journal of Convergence Information Technology, 7(1), pp. 36-44.
• Zhang, N., and Wei G. 2013, Extension of VIKOR method for decision making problem based on hesitant fuzzy set, Applied Mathematical Modelling, 37(7), 4938-4947.
• Rodriguez R.M., Martinez L. and Herrera F. 2012, “Hesitant Fuzzy linguistic term sets for decision making”, IEEE Transactions on Fuzzy Systems 20(1), 109–19.
• Öztaysi B., Cevik Onar S., Boltürk E., Kahraman C., 2015, “Hesitant fuzzy analytic hierarchy process”, Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on, 1-7
• Kahraman C., Boltürk E., Cevik Onar S., Öztayşi B., 2016, “Multi-Attribute Warehouse Location Selection In Humanitarian Logistics Using Hesitant Fuzzy”, International Journal of the Analytic Hierarchy Process 8 (2), 271-298.
• Filev D., Yager R.R., 1998, “On the issue of obtaining OWA operator weights”, Fuzzy sets and systems 94 (2), 157-169.
• Cluzel, F., Yannou, B., Millet, D., Leroy, Y. 2016, “Eco-ideation and eco-selection of R&D projects portfolio in complex systems industries”, Journal of Cleaner Production, 112, pp. 4329-4343.
• Karasakal, E., Aker, P. 2015, “A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem”, Omega.
• Hassanzadeh, F., Nemati, H., Sun, M. 2014, “Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection”, European Journal of Operational Research, 238(1), pp. 41-53.
• Binneman, B., Steyn, H. 2014, “Criteria for selection and gate reviews of technology innovation projects”, South African Journal of Industrial Engineering, 25(1), pp. 117-130
• Güemes-Castorena, D., Uscanga-Castillo, G.I. 2014, “Evaluation tool for technological project selection in the early stage of innovation: Experiences from the development of the application in a technology transfer office”, PICMET 2014 - Portland International Center for Management of Engineering and Technology, Proceedings: Infrastructure and Service Integration, pp. 2836-2842.
• Silva, T., Jian, M., Chen, Y. 2014, “Process analytics approach for R&D project selection”, ACM Transactions on Management Information Systems, 5(4),21