Yıl: 2023 Cilt: 11 Sayı: 1 Sayfa Aralığı: 1 - 10 Metin Dili: İngilizce DOI: 10.21541/apjess.1104601 İndeks Tarihi: 16-06-2023

An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects

Öz:
Research and development (R&D) activities are essential to guarantee continuity of firms, meet customer requirements and keep ahead in competition. R&D project selection constitutes a significant part of project management in order to achieve the desired results and outputs. In this study, an integrated fuzzy multi-criteria group decision making approach is developed for R&D project selection. The problem includes a hierarchical structure of the criteria, uncertainty in evaluating the relative importance of criteria/sub-criteria and rating of candidate projects. The method employs the ordered weighted average (OWA) operator as the aggregation operator, which helps to fully reflect the real behavior of the decision makers in group decision making problems. Fuzzy integral method, which does not require the assumption of the mutual independence of criteria, is used to rank the alternatives. The case study is conducted in a small-sized company in Turkey, which designs and produces special purpose machines. A R&D project selection model is developed to maximize the desired outputs. The results of the analysis show that technological, environmental, marketing, organizational, national and financial issues should be considered simultaneously in the evaluation process. The proposed method is shown to be efficient, generalizable and practical and it has several significant merits compared to the other methods.
Anahtar Kelime: Decision support systems Fuzzy integral Hierarchical decision making OWA R&D project selection.

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
0
0
0
  • [1] R. Bhattacharyya, “A Grey Theory Based Multiple Attribute Approach for R&D Project Portfolio Selection,” Fuzzy Information and Engineering, vol. 7, no. 2, pp. 211–225, 2015.
  • [2] C. Muralidharan, N. Anantharaman, and S. G. Deshmukh, “A multi-criteriagroup decision making model for supplier rating,” The Journal of Supply Chain Management, vol. 38, no. 4, pp. 22–33, 2002.
  • [3] A. D. Henriksen and A. J. Traynor, “ A practical R&D project-selection scoring tool,” IEEE Transactions on Engineering Management, vol. 46, no. 2, pp. 158-170, 1999.
  • [4] R. Bhattacharyya, P. Kumar, P. and S. Kar, “Fuzzy R&D portfolio selection of interdependent projects,” Computers and Mathematics with Applications, vol. 62, no. 10, pp. 3857-3870, 2011.
  • [5] B. Feng, J. Ma and Z. P. Fan, “An integrated method for collaborative R&D project selection: Supporting innovative research teams,” Expert Systems with Applications, vol. 38, no. 5, pp. 5532-5543, 2011.
  • [6] K. Khalili-Damghania and S. Sadi-Nezhad, “A decision support system for fuzzy multi-objective multi-period sustainable project selection,” Computers & Industrial Engineering, vol. 64, no. 4, pp. 1045-1060, 2013.
  • [7] K. Khalili-Damghania and S. Sadi-Nezhad, “A hybrid fuzzy multiple criteria group decision making approach for project selection,” Applied Soft Computing, vol. 13, no. 1, pp. 339-352, 2013.
  • [8] F. Hassanzadeh, H. Nemati and M. Sun, “Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection,” European Journal of Operational Research, vol. 238, no. 1, pp. 41-53, 2014.
  • [9] M. N. M. Arratia, I. F. López, S. E. Schaeffer, and L. Cruz-Reyes, “Static R&D project portfolio selection in public organizations,” Decision Support Systems, vol. 84, pp. 53-63, 2016.
  • [10] F. Cluzel, B. Yannou, D. Millet, and Y. Leroy, “Eco-ideation and eco-selection of R&D projects portfolio in complex systems industries,” Journal of Cleaner Production, vol. 112, no. 5, pp. 4329-4343, 2016.
  • [11] A. Hosseini, O. Lædre, B. Andersen, O. Torp, N. Olsson, and J. Lohne, “Selection Criteria for Delivery Methods for Infrastructure Projects,” Procedia-Social and Behavioral Sciences, vol. 226, pp. 260-268, 2016.
  • [12] E. Karasakal and P. Aker, “A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem,” Omega, vol. 73, pp. 79-92, 2017.
  • [13] H. Jafarzadeh, P. Akbari, and B. Abedin, “A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency – combination of fuzzy QFD and DEA,” Expert Systems with Applications, vol. 110, pp. 237-249, 2018.
  • [14] H. F. Rad and S. M. Rowzan, “Designing a hybrid system dynamic model for analyzing the impact of strategic alignment on project portfolio selection,” Simulation Modelling Practice and Theory, vol. 89, pp. 175-194, 2018.
  • [15] S. Song, F. Yang, and Q. Xia, “Multi-criteria project portfolio selection and scheduling problem based on acceptability analysis,” Computers and Industrial Engineering, vol. 135, pp. 793-799, 2019.
  • [16] F. Liu, Y. W. Chen, J. B. Yang, D. L. Xu, and W. Liu, “Solving multiple- criteria R&D project selection problems with a data-driven evidential reasoning rule,” International Journal of Project Management, vol. 37, no. 1, pp. 87-97, 2019.
  • [17] E. Binici and E. Aksakal, “A new approach to R&D project selection problem and a solution proposal: UTA method,” Pamukkale University Journal of Engineering Sciences, vol. 26, no. 1, pp. 211-226, 2020.
  • [18] V. Mohagheghi, S. M. Mousavi, M. Mojtahedi, S. Newton, “Evaluating large, high-technology project portfolios using a novel interval-valued Pythagorean fuzzy set framework: An automated crane project case study,” Expert Systems with Applications, vol. 162, no. 1, 113007, 2020.
  • [19] P. Liu, B. Zhu, H. Seiti, and L. Yang, “Risk-based decision framework based on R-numbers and best-worst method and its application to research and development project selection,” Information Sciences, vol. 571, no. 1, pp. 303-322, 2021.
  • [20] W. R. W. Mohd and L. Abdullah, “Aggregation methods in group decision making: A decade survey,” Informatica (Slovenia), vol. 41, no. 1, pp. 71–86, 2017.
  • [21] R. R. Yager, “On ordered weighted averaging aggregation operators in multi-criteria decision making,” IEEE Transactions on Systems Man and Cybernetics, vol. 18, no. 1, pp. 183–190, 1988.
  • [22] D. P. Filev and R. R. Yager, “On the issue of obtaining OWA operator weights,” Fuzzy Sets and Systems, vol. 94, pp. 157–169, 1998.
  • [23] M. Dursun, E. E. Karsak, and M. A. Karadayi, “A fuzzy multi-criteria group decision making framework for evaluating health-care waste disposal alternatives,” Expert Systems with Applications, vol. 38, no. 9, pp. 11453-11462, 2011.
  • [24] M. Ying, “Linguistic quantifiers modeled by Sugeno integrals,” Artificial Intelligence, vol. 170, pp. 581-606, 2006.
  • [25] M. Ayub, Choquet and Sugeno Integrals, Master Thesis, Blekinge Institute of Technology, 2009.
  • [26] G. Choquet, “Theory of capacities,” Annales de l'Institut Fourier, vol. 5, pp. 131-295, 1954.
  • [27] M. Sugeno, Theory of fuzzy integrals and its applications, Thesis, Tokyo Institute of Technology, 1974.
  • [28] J. M. Keller, J. Osborn, “Training the fuzzy integral,” International Journal of Approximate Reasoning, vol. 15, no. 1, pp. 1-24, 1996.
  • [29] R. Balachandra, J. H. Friar, “Factors for success in r&d projects and new product innovation: a contextual framework,” IEEE Transactions on Engineering Management, vol. 44, no. 3, pp. 276-287, 1997.
  • [30] C. C. Huang, P. Y. Chu, and Y. H. Chiang, “A fuzzy AHP application in government-sponsored R&D project selection,” Omega, vol. 36, no. 6, pp. 1038-1052, 2008.
  • [31] A. S. Pillai, A. Joshi, and K. S. Rao, “Performance measurement of R&D projects in a multi-project, concurrent engineering environment,” International Journal of Project Management, vol. 20, no. 2, pp. 165-177, 2002.
  • [32] G. Jin, S. Sperandio, and P. Girard, “Selection of design project with the consideration of designers’ satisfaction factors and collaboration ability,” Computers and Industrial Engineering, vol. 131, pp. 66-81, 2019.
  • [33] J. J. Jiang and G. Klein, “Project selection criteria by strategic orientation,” Information and Management, vol. 36, no. 2, pp. 63-75, 1999.
  • [34] S. Freeling, “Fuzzy sets and decision analysis,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 10, pp. 341-354, 1980.
  • [35] T. L. Saaty and L. T. Tran, “On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process,” Mathematical and Computer Modelling, vol. 46, pp. 962-975, 2007.
APA Dursun M, Kılıç M (2023). An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. , 1 - 10. 10.21541/apjess.1104601
Chicago Dursun Mehtap,Kılıç Melike An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. (2023): 1 - 10. 10.21541/apjess.1104601
MLA Dursun Mehtap,Kılıç Melike An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. , 2023, ss.1 - 10. 10.21541/apjess.1104601
AMA Dursun M,Kılıç M An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. . 2023; 1 - 10. 10.21541/apjess.1104601
Vancouver Dursun M,Kılıç M An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. . 2023; 1 - 10. 10.21541/apjess.1104601
IEEE Dursun M,Kılıç M "An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects." , ss.1 - 10, 2023. 10.21541/apjess.1104601
ISNAD Dursun, Mehtap - Kılıç, Melike. "An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects". (2023), 1-10. https://doi.org/10.21541/apjess.1104601
APA Dursun M, Kılıç M (2023). An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. Academic Platform journal of engineering and smart systems (Online), 11(1), 1 - 10. 10.21541/apjess.1104601
Chicago Dursun Mehtap,Kılıç Melike An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. Academic Platform journal of engineering and smart systems (Online) 11, no.1 (2023): 1 - 10. 10.21541/apjess.1104601
MLA Dursun Mehtap,Kılıç Melike An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. Academic Platform journal of engineering and smart systems (Online), vol.11, no.1, 2023, ss.1 - 10. 10.21541/apjess.1104601
AMA Dursun M,Kılıç M An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. Academic Platform journal of engineering and smart systems (Online). 2023; 11(1): 1 - 10. 10.21541/apjess.1104601
Vancouver Dursun M,Kılıç M An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects. Academic Platform journal of engineering and smart systems (Online). 2023; 11(1): 1 - 10. 10.21541/apjess.1104601
IEEE Dursun M,Kılıç M "An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects." Academic Platform journal of engineering and smart systems (Online), 11, ss.1 - 10, 2023. 10.21541/apjess.1104601
ISNAD Dursun, Mehtap - Kılıç, Melike. "An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects". Academic Platform journal of engineering and smart systems (Online) 11/1 (2023), 1-10. https://doi.org/10.21541/apjess.1104601