Selection of Information Management System for Scientific Research Project using Integrated AHP and MULTIMOORA Approaches under Intuitionistic Fuzzy Sets Environment

Abstract

Scientific Research Project Information Management Systems (BAPSIS) are used in order to organize the stages of receiving, evaluating, and accepting or rejecting the project applications in universities, and also to monitor the project status and to coordinate the whole process. The BAPSIS management systems reduce the workload in project operations, eliminate complexities, and allow for transparent and secure monitoring of processes. Therefore, Outsourcing the BAP management systems is the best option to select the suitable BAPSIS information management system, to provide the appropriate selection environment, to minimize the operation costs, and to reduce problematic areas or time-consuming business processes for universities. It is not a very simple decision-making process for university administrations to comply with all constraints and choose the most cost effective system that can maximize efficiency. The purpose in this study is to determine the criteria affecting the decision on BAPSIS system selection for a university in Turkey and to evaluate of possible alternatives according to determined criteria from the perspective of effective process management in universities. In this evaluation, due to effective contribution to modeling ambiguous or indefinite information, an approach in which Intuitionistic Fuzzy Sets are integrated with Group Decision Making based Analytic Hierarchy Process (AHP) and MULTIMOORA methods for the first time. Sensitivity analysis and comparison with existing techniques have also been presented to verify the validity of the result under changing conditions.

Keywords

• Intuitionistic Fuzzy Sets,Group Decision Making,AHP,MULTIMOORA,Scientific Research Projects Information Management System Selection

Sezgisel Bulanık Küme Temelli Entegre AHP ve MULTIMOORA Yöntemleriyle Bilimsel Araştırma Projesi Süreç Yönetim Sistemi Seçimi

Abstract

Üniversitelerde proje başvurularının alınması, değerlendirilmesi ve kabul ya da ret edilmesi gibi aşamaları organize etmek, ayrıca proje durumunun izlenmesini sağlayıp tüm süreci koordine etmek amacıyla, Bilimsel Araştırma Projesi Süreç Yönetim Sistemi (BAPSİS) kullanılır. BAPSİS proje işlemlerindeki iş yükünü azaltır karmaşıklıkları giderir, şeffaf ve güvenli bir şekilde süreçlerin takibine imkân sunar. Bu yüzden, doğru BAPSİS seçilmesinde, uygun seçim ortamının sağlanması, operasyon maliyetlerinin en aza indirilmesi ve üniversiteler için sorun yaratan ya da zaman alan iş süreçlerinin azaltılması için BAPSİS sisteminin dışardan tedarik edilecek bir firmayla yapılmasının en iyi seçenek olduğu görülmüştür. Üniversite yönetimlerinin tüm kısıtlara uymaları ve verimliliği en üst düzeye çıkarabilecek en uygun maliyetli sistemi seçmeleri çok basit bir karar verme süreci değildir. Bu çalışmada, Türkiye'deki bir üniversite için BAPSİS seçim kararını etkileyen ölçütlerin belirlenmesi ve üniversitelerde etkin süreç yönetimi perspektifinden belirlenen ölçütlere göre olası alternatiflerin değerlendirilmesi amaçlanmıştır. Bu değerlendirmede, belirsiz ya da kesin olmayan bilgileri modellemedeki etkin faydasından dolayı Sezgisel Bulanık Kümelerin Grup Karar Verme temelli Analitik Hiyerarşi Süreci (AHP) ve MULTIMOORA yöntemleriyle ilk kez birlikte entegre edildiği bir yaklaşım kullanılmıştır. Elde edilen sonucun değişen koşullar altında geçerliliğini doğrulamak için duyarlılık analizi ve mevcut tekniklerle karşılaştırılması da sunulmuştur.

Keywords

• Sezgisel Bulanık Kümeler,Grup Karar Verme,AHP,MULTIMOORA,Bilimsel Araştırma Projeleri Süreç Yönetim Sistemi Seçimi

References

1. Abdullah, L., & Najib, L. (2014). A new preference scale of intuitionistic fuzzy analytic hierarchy process in multi-criteria decision making problems. Journal of Intelligent & Fuzzy Systems, 26(2), 1039–1049. https://doi.org/10.1007/s00500-014-1519-y
2. Abdullah, L., & Najib, L. (2016a). A new preference scale mcdm method based on interval-valued intuitionistic fuzzy sets and the analytic hierarchy process. Soft Computing, 20(2), 511–523. https://doi.org/10.1007/s00500-014-1519-y
3. Abdullah, L., & Najib, L. (2016b). Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia. International Journal of Sustainable Energy, 35(4), 360–377. https://doi.org/10.1080/14786451.2014.907292
4. Atalay, K. D., & Can, G. F. (2018). A new hybrid intuitionistic approach for new product selection. Soft Computing, 22(8), 2633–2640. https://doi.org/10.1007/s00500-017-2517-7
5. Atanassov, K. . (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
6. Ayağ, Z., & Yücekaya, A. (2019). A Fuzzy ANP-Based GRA Approach to Evaluate ERP Packages. International Journal of Enterprise Information Systems, 15(1), 45–68. https://doi.org/10.4018/IJEIS.2019010103
7. Baki, B., & Çakar, K. (2005). Determining the ERP package‐selecting criteria: The case of Turkish manufacturing companies. Business Process Management Journal, 11(1), 75–86. https://doi.org/10.1108/14637150510578746
8. Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Systems with Applications, 36(8), 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039

9. Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by multimoora as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01
10. Brauers, W. K. M., & Zavadskas, E. K. (2011). Multimoora Optimization Used to Decide on a Bank Loan to Buy Property. Technological and Economic Development of Economy, 17(1), 174–188. https://doi.org/10.3846/13928619.2011.560632
11. Büyüközkan, G., Feyzioğlu, O., & Göçer, F. (2016). Evaluation of hospital web services using intuitionistic fuzzy AHP and intuitionistic fuzzy VIKOR. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 607–611). IEEE. https://doi.org/10.1109/IEEM.2016.7797947
12. Büyüközkan, G., & Gocer, F. (2017). An Intuitionistic Fuzzy MCDM Approach for Effective Hazardous Waste Management. In Intelligence Systems in Environmental Management: Theory and Applications (Vol. 113, pp. 21–40). Springer. https://doi.org/10.1007/978-3-319-42993-9_2
13. Büyüközkan, G., & Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, 52(March), 1222–1238. https://doi.org/10.1016/j.asoc.2016.08.051
14. Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain. Applied Soft Computing, 69(C), 634–654. https://doi.org/10.1016/j.asoc.2018.04.040
15. Büyüközkan, G., Göçer, F., & Feyzioğlu, O. (2018a). Cloud computing technology selection based on interval-valued intuitionistic fuzzy MCDM methods. Soft Computing, 22(15), 5091–5114. https://doi.org/10.1007/s00500-018-3317-4
16. Büyüközkan, G., Göçer, F., & Feyzioğlu, O. (2018b). Cloud Computing Technology Selection Based on Interval Valued Intuitionistic Fuzzy COPRAS. In J. Kacprzyk, E. Szmidt, S. Zadrożny, K. T. Atanassov, & M. Krawczak (Eds.), Advances in Intelligent Systems and Computing (Proceedings of: EUSFLAT- 2017) (Vol. 641, pp. 318–329). Springer International Publishing. https://doi.org/10.1007/978-3-319-66830-7_29
17. Büyüközkan, G., Göçer, F., & Karabulut, Y. (2019). A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Measurement, 134, 66–82. https://doi.org/10.1016/j.measurement.2018.10.041
18. Büyüközkan, G., & Ruan, D. (2008a). Evaluation of software development projects using a fuzzy multi-criteria decision approach. Mathematics and Computers in Simulation, 77(5–6), 464–475. https://doi.org/10.1016/j.matcom.2007.11.015
19. Büyüközkan, G., & Ruan, D. (2008b). Evaluation of software development projects using a fuzzy multi-criteria decision approach. Mathematics and Computers in Simulation, 77(5–6), 464–475. https://doi.org/10.1016/j.matcom.2007.11.015
20. Çakır, E. (2018). Elektronik Belge Yönetim Sistemi (EBYS) Yazılımı Seçiminde Çok Kriterli Karar Verme Yöntemleri: Bir Belediye Örneği. Business, Economics and Management Research Journal, 1(August), 15–30.
21. Demirci, J. R., & Bogen, D. L. (2017). Feasibility and acceptability of a mobile app in an ecological momentary assessment of early breastfeeding. Maternal & Child Nutrition, 13(3), e12342. https://doi.org/10.1111/mcn.12342
22. Efe, B., Kurt, M., & Efe, Ö. F. (2017). An integrated intuitionistic fuzzy set and mathematical programming approach for an occupational health and safety policy. Gazi University Journal of Science, 30(2), 73–95.
23. Eyüboğlu, F. (2012). Süreç Yönetimi ve Süreç İyileştirme (2. Baskı). Sistem Yayıncılık.
24. Hwang, C. L., & Yoon, K. (1981). Multiple Attribute Decision Making-Methods and Application. New York: Springer.
25. Iranpour, A., Sayyadi Tooranloo, H., & Ayatollah, A. S. (2018). A model for supplier evaluation and selection based on integrated interval-valued intuitionistic fuzzy AHP-TOPSIS approach. International Journal of Mathematics in Operational Research, 13(3), 401. https://doi.org/10.1504/IJMOR.2018.10016119
26. Karasan, A., Erdogan, M., & Ilbahar, E. (2018). Prioritization of production strategies of a manufacturing plant by using an integrated intuitionistic fuzzy AHP & TOPSIS approach. Journal of Enterprise Information Management, 31(4), 510–528. https://doi.org/10.1108/JEIM-01-2018-0001
27. Kilic, H. S., Zaim, S., & Delen, D. (2014). Development of a hybrid methodology for ERP system selection: The case of Turkish Airlines. Decision Support Systems, 66, 82–92. https://doi.org/10.1016/j.dss.2014.06.011
28. Kilic, H. S., Zaim, S., & Delen, D. (2015). Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Systems with Applications, 42(5), 2343–2352. https://doi.org/10.1016/j.eswa.2014.10.034
29. Kuo, R. J., Hong, S. Y., & Huang, Y. C. (2010). Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection. Applied Mathematical Modelling, 34(12), 3976–3990. https://doi.org/10.1016/j.apm.2010.03.033
30. Kutlu, B., & Akpinar, E. (2009). ERP Software Selection using Fuzzy Methodology: A Case Study. Journal of Applied Sciences, 9(18), 3378–3384.
31. Li, Y.-L., Wang, R., & Chin, K.-S. (2019). New failure mode and effect analysis approach considering consensus under interval-valued intuitionistic fuzzy environment. Soft Computing. https://doi.org/10.1007/s00500-018-03706-5
32. Liao, H., & Xu, Z. (2015). Consistency of the fused intuitionistic fuzzy preference relation in group intuitionistic fuzzy analytic hierarchy process. Applied Soft Computing, 35, 812–826. https://doi.org/10.1016/j.asoc.2015.04.015
33. Mylonas, A., Kastania, A., & Gritzalis, D. (2013). Delegate the smartphone user? Security awareness in smartphone platforms. Computers & Security, 34, 47–66. https://doi.org/10.1016/j.cose.2012.11.004
34. Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. Belgrade: Faculty of Civil Engineering.
35. Otay, İ., Oztaysi, B., Cevik Onar, S., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90–106. https://doi.org/10.1016/j.knosys.2017.06.028
36. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.
37. Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resources allocation. McGraw-Hill.
38. Saaty, T. L. (1996). The analytic network process. RWS Publications, Expert Choice, Inc.
39. Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stochastic Environmental Research and Risk Assessment, 23(1), 75–91. https://doi.org/10.1007/s00477-007-0197-z
40. Smith, A., de Salas, K., Lewis, I., & Schüz, B. (2017). Developing smartphone apps for behavioural studies: The AlcoRisk app case study. Journal of Biomedical Informatics, 72, 108–119. https://doi.org/10.1016/j.jbi.2017.07.007
41. Tooranloo, H. S., & Iranpour, A. (2017). Supplier selection and evaluation using interval-valued intuitionistic fuzzy AHP method. International Journal of Procurement Management, 10(5), 539. https://doi.org/10.1504/IJPM.2017.086399
42. Wang, W., & Xin, X. (2005). Distance measure between intuitionistic fuzzy sets. Pattern Recognition Letters, 26(13), 2063–2069. https://doi.org/10.1016/j.patrec.2005.03.018
43. Wang, Y., & Xu, Z. (2018). Evaluation of the Human Settlement in Lhasa with Intuitionistic Fuzzy Analytic Hierarchy Process. International Journal of Fuzzy Systems, 20(1), 29–44. https://doi.org/10.1007/s40815-017-0422-y
44. Xu, Z. (2011). Intuitionistic Preference Modeling and Interactive Decision Making. Springer. https://doi.org/10.1007/978-3-642-28403-8
45. Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy analytic hierarchy process. Fuzzy Systems, IEEE Transactions On, 22(4), 1. https://doi.org/10.1109/TFUZZ.2013.2272585
46. Xu, Z., & Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International Journal of General Systems, 35(4), 417–433. https://doi.org/10.1080/03081070600574353
47. Yu, Y., Darko, A., Chan, A. P. C., Chen, C., & Bao, F. (2018). Evaluation and Ranking of Risk Factors in Transnational Public–Private Partnerships Projects: Case Study Based on the Intuitionistic Fuzzy Analytic Hierarchy Process. Journal of Infrastructure Systems, 24(4), 04018028. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000448
48. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
49. Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multi-criteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3), 131–139.
50. Zavadskas, Edmundas Kazimieras, Antucheviciene, J., Hajiagha, S. H. R., & Hashemi, S. S. (2015). The interval-valued intuitionistic fuzzy MULTIMOORA method for group decision making in engineering. Mathematical Problems in Engineering, 2015(2). https://doi.org/10.1155/2015/560690
51. Zhang, C., Chen, C., Streimikiene, D., & Balezentis, T. (2019). Intuitionistic fuzzy MULTIMOORA approach for multi-criteria assessment of the energy storage technologies. Applied Soft Computing, 79, 410–423. https://doi.org/10.1016/j.asoc.2019.04.008
52. Zhao, H., You, J. X., & Liu, H. C. (2016). Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft Computing, 1–13. https://doi.org/10.1007/s00500-016-2118-x