1. Home
  2. Issues
  3. Volume 34, Issue 3 (2023)
  4. An Intuitionistic Fuzzy Consensus WASPAS ...

Informatica


An Intuitionistic Fuzzy Consensus WASPAS Method for Assessment of Open-Source Software Learning Management Systems
Volume 34, Issue 3 (2023), pp. 529–556
Pub. online: 28 August 2023      Type: Research Article      Open accessOpen Access
Received
1 May 2023
Accepted
1 August 2023
Published
28 August 2023

Abstract

Ineffective evaluation of open-source software learning management system (OSS-LMS) packages can negatively impact organizational effectiveness. Clients may struggle to select the best OSS-LMS package from a wide range of options, leading to a complex multi-criteria group decision-making (MCGDM) problem. This evaluates OSS-LMS packages based on several criteria like usability, functionality, e-learning standards, reliability, activity tracking, course development, assessment, backup and recovery, error reporting, efficiency, operating system compatibility, computer-managed instruction, authentication, authorization, troubleshooting, maintenance, upgrading, and scalability. Handling uncertain data is a vital aspect of OSS-LMS package evaluation. To tackle MCGDM issues, this study presents a consensus weighted sum product (c-WASPAS) method which is applied to an educational OSS-LMS package selection problem to evaluate four OSS-LMS packages, namely ATutor, eFront, Moodle, and Sakai. The findings indicate that the priority order of alternatives is Moodle > Sakai > eFront > ATutor and, therefore, MOODLE is the best OSS-LMS package for the case study. A sensitivity analysis of criteria weights is also conducted, as well as a comparative study, to demonstrate the effectiveness of the proposed method. It is essential to note that proper OSS-LMS package evaluation is crucial to avoid negative impacts on organizational performance. By addressing MCGDM issues and dealing with uncertain information, the c-WASPAS method presented in this study can assist clients in selecting the most appropriate OSS-LMS package from multiple alternatives. The findings of this study can benefit educational institutions and other organizations that rely on OSS-LMS packages to run their operations.

References

 
Abdullateef, B.N., Elias, N.F., Mohamed, H., Zaidan, A.A., Zaidan, B.B. (2015). Study on open source learning management systems: a survey, profile and taxonomy. Journal of Theoretical and Applied Information Technology, 82, 93–105.
 
Abdullateef, B.N., Elias, N.F., Mohamed, H. (2016a). Open source learning management system: a comparative study. Journal of Engineering and Applied Sciences, 11(3), 519–522.
 
Abdullateef, B.N., Elias, N.F., Mohamed, H., Zaidan, A.A., Zaidan, B.B. (2016b). An evaluation and selection problems of OSS-LMS packages. SpringerPlus, 5, 248. https://doi.org/10.1186/s40064-016-1828-y.
 
Adewumi, A., Misra, S., Omoregbe, N., Sanz, L.F. (2019). FOSSES: framework for open-source software evaluation and selection. Software: Practice and Experiment, 49(5), 780–812.
 
Al Amoush, A.B., Sandhu, K. (2020). Digital learning management systems case study: instructors’ perspective. In: Delello, J., McWhorter, R. (Eds.), Disruptive and Emerging Technology Trends Across Education and the Workplace. IGI Global, pp. 143–168. https://doi.org/10.4018/978-1-7998-2914-0.ch006.
 
Albarrak, A.I., Aboalsamh, H.A., Abouzahra, M. (2010). Evaluating learning management systems for University medical education. In: Proceedings of the International Conference on Education, Management, and Technology, Cairo, Egypt, 2010, pp. 672–677. https://doi.org/10.1109/ICEMT.2010.5657569.
 
Alturki, U., Aldraiweesh, A. (2021). Application of Learning Management System (LMS) during the COVID-19 pandemic: a sustainable acceptance model of the expansion technology approach. Sustainability, 13(19), 10991. https://doi.org/10.3390/su131910991.
 
Arh, T., Blazic, B.J. (2007). A multi-attribute decision support model for learning management systems evaluation. In: First International Conference on the Digital Society, ICDS’07, Guadeloupe, French Caribbean, 2007, p. 11. https://doi.org/10.1109/ICDS.2007.1.
 
Arisantoso, S.M.H., Sanwasih, M., Shalahudin, M.I. (2023). Multi-criteria decision making using the WASPAS method in Webcam selection decision support systems. International Journal of Informatics and Computer Science, 7(1), 1–10.
 
Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
 
Awang, N.B., Darus, M.Y.B. (2012). Evaluation of an open source learning management system: Claroline. Procedia – Social and Behavioral Sciences, 67, 416–426.
 
Badalpur, M., Nurbakhsh, E. (2021). An application of WASPAS method in risk qualitative analysis: a case study of a road construction project in Iran. International Journal of Construction Management, 21(9), 910–918. https://doi.org/10.1080/15623599.2019.1595354.
 
Bid, S., Siddique, G. (2019). Human risk assessment of Panchet Dam in India using TOPSIS and WASPAS Multi-Criteria Decision-Making (MCDM) methods. Heliyon, 5(6), e01956. https://doi.org/10.1016/j.heliyon.2019.e01956.
 
Buran, B., Erçek, M. (2022). Public transportation business model evaluation with Spherical and Intuitionistic Fuzzy AHP and sensitivity analysis. Expert Systems with Applications, 204, 117519.
 
Büyüközkan, G., Feyzioğlu, O., Görçer, F. (2018). Selection of sustainable urban transportation alternatives using an integrated intuitionistic fuzzy Choquet integral approach. Transportation Research Part D: Transportation and Environment, 58, 186–207.
 
Çakır, E., Taş, M.A. (2023). Circular intuitionistic fuzzy decision making and its application. Expert Systems with Applications, 225, 120076.
 
Caminero, A.C., Hernandez, R.D., Ros, S., Robles-Gómez, A., Tobarra, L. (2013). Choosing the right LMS: a performance evaluation of three open-source LMS. In: 2013 IEEE Global Engineering Education Conference (EDUCON), Berlin, Germany, 2013, pp. 287–294. https://doi.org/10.1109/EduCon.2013.6530119.
 
Cavus, N. (2007). The Effects of Using Learning Management Systems on Collaborative Learning for Teaching Programming Languages. Dissertation, Near East University, Graduate School of Applied Sciences, Department of Computer Information Systems, Nicosia, Cyprus. http://docs.neu.edu.tr/library/4956059057.pdf.
 
Çetin, A., Işık, A.H., Güler, İ. (2010). Learning management system selection with analytic hierarchy process. In: 13th International Conference on Interactive Computer aided Learning (ICL2010), Hasselt, Belgium, pp. 921–926.
 
Davoudabadi, R., Mousavi, S.M., Mohagheghi, V. (2020). A new last aggregation method of multi-attributes group decision-making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty. Knowledge and Information Systems, 62, 1371–1391. https://doi.org/10.1007/s10115-019-01390-x.
 
de Assis, G.S., dos Santos, M., Basilio, M.P. (2023). Use of the WASPAS method to select suitable helicopters for aerial activity carried out by the military police of the state of Rio de Janeiro. Axioms, 12(1), 77. https://doi.org/10.3390/axioms12010077.
 
Deb, P.P., Bhattacharya, D., Chatterjee, I., Saha, A., Mishra, A.R., Ahammad, S.H. (2022). A decision-making model with intuitionistic fuzzy information for selection of enterprise resource planning systems. In: IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2022.3215608.
 
Deveci, M., Canıtez, F., Gökaşar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Societies, 41, 777–791. https://doi.org/10.1016/j.scs.2018.05.034.
 
Deveci, M., Krishankumar, R., Gokasar, I., Deveci, R.T. (2022). Prioritization of healthcare systems during pandemics using Cronbach’s measure based fuzzy WASPAS approach. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04714-3.
 
Deveci, M., Erdogan, N., Pamucar, D., Kucuksari, S., Cali, U. (2023). A rough Dombi Bonferroni based approach for public charging station type selection. Applied Energy, 345, 121258. https://doi.org/10.1016/j.apenergy.2023.121258.
 
Dong, Y., Xu, Y., Li, H., Feng, B. (2010). The OWA-based consensus operator under linguistic representation models using position indexes. European Journal of Operations Research, 203(2), 455–463.
 
Dorfeshan, Y., Mousavi, S.M. (2020). A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods (with an application in aircraft maintenance planning). Neural Computing and Applications, 32, 3367–3385.
 
Edrees, M.E. (2013). eLearning 2.0: learning management systems readiness. In: 2013 Fourth International Conference on e-Learning “Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity”, Manama, Bahrain, 2013, pp. 90–96. https://doi.org/10.1109/ECONF.2013.57.
 
Filip, F.G. (2021). Automation and computers and their contribution to human well-being and resilience. Studies in Informatics and Control, 30(4), 5–18.
 
Garg, H., Rani, D. (2022). An efficient intuitionistic fuzzy MULTIMOORA approach based on novel aggregation operators for the assessment of solid waste management techniques. Applied Intelligence, 52, 4330–4363.
 
Gohain, B., Chutia, R., Dutta, P. (2022). Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. International Journal of Intelligent Systems, 37(3), 2458–2501.
 
Gokasar, I., Deveci, M., Isik, M., Daim, T., Zaidan, A.A., Smarandache, F. (2023a). Evaluation of the alternatives of introducing electric vehicles in developing countries using Type-2 neutrosophic numbers based RAFSI model. Technological Forecasting and Social Change, 192, 122589. https://doi.org/10.1016/j.techfore.2023.122589.
 
Gokasar, I., Pamucar, D., Deveci, M., Gupta, B.B., Martinez, L., Castillo, O. (2023b). Metaverse integration alternatives of connected autonomous vehicles with self-powered sensors using fuzzy decision making model. Information Sciences, 642, 119192. https://doi.org/10.1016/j.ins.2023.119192.
 
Gökmener, S., Oğuz, E., Deveci, M., Göllü, K. (2023). Site selection for floating photovoltaic system on dam reservoirs using sine trigonometric decision making model. Ocean Engineering, 281, 114820. https://doi.org/10.1016/j.oceaneng.2023.114820.
 
Gong, Z., Zhang, H., Forrest, J., Li, L., Xu, X. (2015). Two consensus models based on the minimum cost and maximum return regarding either all individuals or one individual. European Journal of Operations Research, 240(1), 183–192.
 
Graf, S., List, B. (2005). An evaluation of open source e-learning platforms stressing adaptation issues. In: Fifth IEEE International Conference on Advanced Learning Technologies (ICALT’05), Kaohsiung, Taiwan, pp. 163–165. https://doi.org/10.1109/ICALT.2005.54. Retrieved December 3, 2007, from http://www.campussource.de/aktuelles/docs/icalt2005.pdf.
 
Gundogdu, F.K., Kahraman, C. (2019). Extension of WASPAS with spherical fuzzy sets. Informatica, 30(2), 269–292.
 
Handayani, N., Heriyani, N., Septian, F., Alexander, A.D. (2023). Multi-criteria decision making using the WASPAS method for online english course selection. Jurnal Teknoinfo, 17(1), 260–270.
 
Herrera, F., Herrera-Viedma, E., Verdegay, J.L. (1996). A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and Systems, 78(1), 73–87.
 
Herrera-Viedma, E., Cabrerizo, F.J., Kacprzyk, J., Pedrycz, W. (2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17, 4–13.
 
Hezam, I.M., Mishra, A.R., Rani, P., Cavallaro, F., Saha, A., Ali, J., Strielkowski, W., Štreimikienė, D. (2022). A hybrid intuitionistic fuzzy-MEREC-RS-DNMA method for assessing the alternative fuel vehicles with sustainability perspectives. Sustainability, 14(9), 5463. https://doi.org/10.3390/su14095463.
 
Hezam, I.M., Rani, P., Mishra, A.R., Alshamrani, A. (2023a). An intuitionistic fuzzy entropy-based gained and lost dominance score decision-making method to select and assess sustainable supplier selection. AIMS Mathematics, 8(5), 12009–12039.
 
Hezam, I.M., Mishra, A.R., Rani, P., Saha, A., Smarandache, F., Pamucar, D. (2023b). An integerated decision support framework using single-valued neutrosophic-MASWIP-COPRAS for sustainability assessment of bioenergy production technologies. Expert System With Applications, 211, 118674. https://doi.org/10.1016/j.eswa.2022.118674.
 
Hock, S.Y., Omar, R., Mahmud, M. (2015). Comparing the usability and users acceptance of open sources Learning Management System (LMS). International Journal of Scientific Research, 5(4), 1–5.
 
Hultin, J. (2007). Learning Management Systems (LMS): a review. Retrieved December 17, 2007, from http://se2.isn.ch/serviceengine/FileContent?serviceID=18&fileid=09FD0FDC-723F-5E63-CCDB6AFCDA98BCC8&lng=en.
 
Işik, A.H., İnce, M., Yiğit, T. (2015). A fuzzy AHP approach to select learning management system. International Journal of Computer Theory and Engineering, 7(6), 499–502.
 
Ivanović, B., Saha, A., Stević, Ž., Puška, A., Zavadskas, E.K. (2022). Selection of truck mixture concrete pump using novel MEREC-DNMARCOS model. Archives of Civil and Mechanical Engineering, 22, 173. https://doi.org/10.1007/s43452-022-00491-9.
 
Jadav, A., Sonar, R. (2011). Framework for evaluation and selection of the software packages: a hybrid knowledge based system approach. Journal of System Software, 84(8), 1394–1407.
 
Kahraman, C., Onar, S.C., Oztaysi, B., Ilbahar, E. (2019). Selection among GSM operators using pythagorean fuzzy WASPAS method. Journal of Multi-Valued Logic & Soft Computing, 33(4–5), 459–469.
 
Kahraman, Y.R. (2002). Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models (No. AFIT/GOR/ENS/02-10). Master’s thesis, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, United States
 
Karagöz, E., Oral Güney, L.Ö, Kaya, O.H., Tecim, V. (2017). LMS selection process for effective distance education system in organizations. KnE Social Sciences, 1(2), 343–356.
 
Keshavarz-Ghorabaee, M., Govindan, K., Amiri, M., Zavadskas, E.K., Antuchevičienė, J. (2019). An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. Journal of Environmental Engineering and Landscape Management, 27(4), 187–200. https://doi.org/10.3846/jeelm.2019.11367.
 
Kljun, M., Vicic, J., Kavsek, B., Kavcic, A. (2007). Evaluating comparisons and evaluations of learning management systems. In: 2007 29th International Conference on Information Technology Interfaces, Cavtat, Croatia, 2007, pp. 363–368. https://doi.org/10.1109/ITI.2007.4283797.
 
Kirkwood, C.W. (1997). Strategic Decision Making: Multi-Objective Decision Analysis with Spreadsheets. Duxbury Press, Belmon.
 
Krishankumar, R., Subrajaa, L.S., Ravichandran, K.S., Kar, S., Saeid, A.B. (2019). A framework for multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic term set. International Journal of Fuzzy Systems, 21, 1130–1143. https://doi.org/10.1007/s40815-019-00618-w.
 
Krishankumar, R., Garg, H., Arun, K., Saha, A., Ravichandran, K.S., Kar, S. (2021). An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information. Complex and Intelligent Systems, 7, 2281–2298.
 
Kumari, R., Mishra, A.R. (2020). Multi-criteria COPRAS method based on parametric measures for intuitionistic fuzzy sets: application of green supplier selection. Iranian Journal of Science and Technology, Transactions in Electrical Engineering, 44, 1645–1662. https://doi.org/10.1007/s40998-020-00312-w.
 
Liao, H.C., Xu, Z.S., Zeng, X.J., Xu, D.L. (2016). An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations. Information Sciences, 329, 274–286.
 
Liu, D., Huang, A. (2020). Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q-rung orthopair fuzzy sets based on correlation measure. International Journal of Intelligent Systems, 35(3), 494–528. https://doi.org/10.1002/int.22215.
 
Liu, P., Saha, A., Mishra, A.R., Rani, P., Dutta, D., Baidya, J. (2022). A BCF-CRITIC-WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators. Journal of Ambient Intelligence and Humanized Computing, 14, 11909–11933. https://doi.org/10.1007/s12652-022-03745-9.
 
Machado, M., Tao, E. (2007). Blackboard vs. moodle: comparing user experience of learning management systems. In: 2007 37th Annual Frontiers In Education Conference – Global Engineering: Knowledge Without Borders, Opportunities Without Passports, Milwaukee, WI, USA, 2007, pp. S4J-7–S4J-12. https://doi.org/10.1109/FIE.2007.4417910.
 
Mishra, A.R., Rani, P. (2018). Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy. Group Decision and Negotiation, 27, 1047–1078. https://doi.org/10.1007/s10726-018-9593-7.
 
Mishra, A.R., Singh, R.K., Motwani, D. (2019a). Multi-criteria assessment of cellular mobile telephone service providers using intuitionistic fuzzy WASPAS method with similarity measures. Granular Computing, 4, 511–529. https://doi.org/10.1007/s41066-018-0114-5.
 
Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A. (2019b). A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. Journal of Cleaner Production, 238, 117901. https://doi.org/10.1016/j.jclepro.2019.117901.
 
Mishra, A.R., Sisodia, G., Pardasani, K.R., Sharma, K. (2019c). Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology. Iranian Journal of Fuzzy Systems, 17(4), 55–68.
 
Mishra, A.R., Mardani, A., Rani, P., Zavadskas, E.K. (2020). A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. Journal of Cleaner Production, 272, 122807.
 
Mishra, A.R., Rani, P., Cavallaro, F., Heza, I.M. (2023). Intuitionistic fuzzy fairly operators and additive ratio assessment-based integrated model for selecting the optimal sustainable industrial building options. Scientific Reports, 13, 5055. https://doi.org/10.1038/s41598-023-31843-x.
 
Mohagheghi, V., Mousavi, S.M. (2020). D-WASPAS: addressing social cognition in uncertain decision-making with an application to a sustainable project portfolio problem. Cognitive Computation, 12, 619–641. https://doi.org/10.1007/s12559-019-09679-3.
 
Mukherjee, S. (2017). Selection of alternative fuels for sustainable urban transportation under multi-criteria intuitionistic fuzzy environment. Fuzzy Information and Engineering, 9, 117–135.
 
Natarajan, M. (2015). Evaluation methods for e-learning: an analytical study. SSARS International Journal of Library and Information Sciences, 1(1), 1–14.
 
Palanisami, D., Mohan, N., Ganeshkumar, L. (2022). A new approach of multi-modal medical image fusion using intuitionistic fuzzy set. Biomedical Signal Processing and Control, 77, 103762. https://doi.org/10.1016/j.bspc.2022.103762.
 
Pamučar, D., Sremac, S., Stević, Ž., Ćirovic, G., Tomić, D. (2019). New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods. Neural Computing and Applications, 31, 5045–5068. https://doi.org/10.1007/s00521-018-03997-7.
 
Petrovas, A., Bausys, R., Zavadskas, E. (2023). Gestalt principles governed fitness function for genetic Pythagorean neutrosophic WASPAS game scene generation. International Journal of Computers Communications & Control, 18(4), 5475. https://doi.org/10.15837/ijccc.2023.4.5475.
 
Ramesh, V.M., Ramanathan, C. (2013). A rubric to evaluate learning management systems. In: Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Bali, Indonesia, 2013, pp. 73–77. https://doi.org/10.1109/TALE.2013.6654402.
 
Rani, P., Mishra, A.R. (2020). Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using q-rung orthopair fuzzy sets. Sustainable Production and Consumption, 24, 90–104. https://doi.org/10.1016/j.spc.2020.06.015.
 
Rani, P., Mishra, A.R., Ansari, M.D., Ali, J. (2021). Assessment of performance of telecom service providers using intuitionistic fuzzy grey relational analysis framework (IF-GRA). Soft Computing, 25, 1983–1993. https://doi.org/10.1007/s00500-020-05269-w.
 
Rasoulzadeh, M., Edalatpanah, S.A., Fallah, M., Najafi, S.E. (2022). A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem. Decision Making: Applications in Management and Engineering, 5(2), 241–259.
 
Rouyendegh, B.D., Yildizbasi, A., Üstünyer, P. (2020). Intuitionistic fuzzy TOPSIS method for green supplier selection problem. Soft Computing, 24, 2215–2228. https://doi.org/10.1007/s00500-019-04054-8.
 
Rudnik, K., Bocewicz, G., Kucińska-Landwójtowicz, A., Czabak-Górska, I.D. (2021). Ordered fuzzy WASPAS method for selection of improvement projects. Expert System with Applications, 169, 114471. https://doi.org/10.1016/j.eswa.2020.114471.
 
Saha, A., Dutta, D., Kar, S. (2021a). Some new hybrid hesitant fuzzy weighted aggregation operators based on Archimedean and Dombi operations for multi-attribute decision making. Neural Computing and Applications, 33, 8753–8776. https://doi.org/10.1007/s00521-020-05623-x.
 
Saha, A., Senapati, T., Yager, R.R. (2021b). Hybridizations of Generalized Dombi operators and Bonferroni mean operators under Dual probabilistic linguistic environment for group decision-making. International Journal of Intelligent Systems, 36(11), 6645–6679.
 
Saha, A., Mishra, A.R., Rani, P., Hezam, I.M., Cavallaro, F. (2022). A q-rung orthopair fuzzy FUCOM double normalization based multi-aggregation method for healthcare waste treatment method selection. Sustainability, 14(7), 4171. https://doi.org/10.3390/su14074171.
 
Saha, A., Pamucar, D., Gorcun, O.F., Mishra, A.R. (2023a). Warehouse site selection for the automotive industry using a Fermatean fuzzy based decision-making approach. Expert System with Applications, 211), 118497. https://www.sciencedirect.com/science/article/abs/pii/S0957417422015809.
 
Saha, A., Simic, V., Dabic-Miletic, S., Senapati, T., Yager, R.R., Devechi, M. (2023b). Evaluation of propulsion technologies for sustainable road freight distribution using a dual probabilistic linguistic group decision-making approach. In: IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2023.3253300. https://ieeexplore.ieee.org/document/10077108.
 
Santiago, B.J., Olivares Ramírez, J.M., Rodríguez-Reséndiz, J., Dector, A., García, R., González-Durán, J.E.E., Sánchez, F.F. (2020). Learning management system-based evaluation to determine academic efficiency performance. Sustainability, 12(10), 4256. https://doi.org/10.3390/su12104256.
 
Schitea, D., Deveci, M., Iordache, M., Bilgili, K., Akyurt, I.Z., Iordache, I. (2019). Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS. International Journal of Hydrogen Energy, 44(16), 8585–8600. https://doi.org/10.1016/j.ijhydene.2019.02.011.
 
Semenas, R., Bausys, R., Zavadskas, E.K. (2021). A novel environment exploration strategy by m-generalised q-neutrosophic WASPAS. Studies in Informatics and Control, 30(3), 19–28.
 
Senapati, T., Chen, G. (2022). Picture fuzzy WASPAS technique and its application in multi-criteria decision-making. Soft Computing, 26, 4413–4421. https://doi.org/10.1007/s00500-022-06835-0.
 
Senapati, T., Simic, V., Saha, A., Dobrodolac, M., Rong, Y., Tirkolaee, E.B. (2023). Intuitionistic fuzzy power Aczel-Alsina model for prioritization of sustainable transportation sharing practices. Engineering Applications of Artificial Intelligence, 119, 105716.
 
Sharma, R., Pradhan, S. (2020). Investigation of machinability criteria during micro-abrasive finishing of SUS-304L steel using fuzzy combined with WASPAS approach. Journal of the Brazilian Society of Mechanical Science and Engineering, 42, 116. https://doi.org/10.1007/s40430-020-2198-5.
 
Srdevic, B., Pipan, M., Srdevic, Z., Arh, T. (2012). AHP supported evaluation of LMS quality. In: Proceedings of the International Workshop on the Interplay between User Experience Evaluation and Software Development, Copenhagen, Denmark, 2012, pp. 52–57.
 
Stanujkic, D., Karabasevic, D. (2018). An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: a case of website evaluation. Operations Research in Engineering Sciences: Theory and Applications, 1(1), 29–39. https://doi.org/10.31181/oresta19012010129s.
 
Tugrul, F. (2022). Evaluation of papers according to offset print quality: the intuitionistic fuzzy based multi criteria decision making mechanism. Pigment & Resin Technology. https://doi.org/10.1108/PRT-04-2022-0059.
 
Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., Zavadskas, E.K., Smarandache, F., Brauers, W.K.M. (2021). Developing of a novel integrated MCDM MULTIMOOSRAL approach for supplier selection. Informatica, 32(1), 145–161.
 
van Rooij, S.W. (2011). Higher education sub-cultures and open sources adoption. Computer Education, 57(1), 1171–1183.
 
van Rooij, S.W. (2012). Open sources learning management systems: a predictive model for higher education. Journal of Computer Assisted Learning, 28(2), 114–125.
 
Wang, Y., Zhang, Z., Sun, H. (2018). Assessing customer satisfaction of urban rail transit network in Tianjin based on intuitionistic fuzzy group decision model. Discrete Dynamics in Nature and Society, 2018, 4205136. https://doi.org/10.1155/2018/4205136.
 
Waynet Inc. (2007). COL LMS open source. Under License of Commonwealth of Learning. Retrieved December 4, 2007, from http://www.col.org/colweb/webdav/site/myjahiasite/shared/docs/03LMSOpenSource.pdf.
 
Wu, Z.B., Xu, J.P. (2016). Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega, 65, 28–40.
 
Wu, X.L., Liao, H.C. (2019). A consensus-based probabilistic linguistic gained and lost dominance score method. European Journal of Operations Research, 272(3), 1017–1027.
 
Wyles, R. (2007). Open source software and the New Zealand education system: a response to roy. Journal of Distance Learning, 10(1), 36–41.
 
Yan, B., Rong, Y., Yu, L., Huang, Y. (2022). A hybrid intuitionistic fuzzy group decision framework and its application in urban rail transit system selection. Mathematics, 10(12), 2133.
 
Yuan, Y., Yang, Y. (2021). Dynamic multiple criteria group decision-making method based on intuitionistic fuzzy information. Journal of Control and Decision, 9(4), 397–406.
 
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
 
Zaidan, A.A., Zaidan, B.B., Al-Haiqi, A., Kiah, M.L.M., Hussain, M., Abdulnabi, M. (2015). Evaluation and selection of open source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390–404.
 
Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics and Electrical Engineering, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810.
 
Zhang, B.W., Liang, H.M., Zhang, G.Q. (2018). Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets. Information Fusion, 42, 12–23.

Biographies

Deb Partha Pratim
https://orcid.org/0000-0001-7905-3911

P.P. Deb is an assistant professor and former head of the Department of Computer Science and Engineering Department at the Techno College of Engineering Agartala (TCEA), India. He has earned his M.Tech from Maulana Abul Kalam Azad University OF Technology (formerly West Bengal University of Technology), West Bengal, in 2013, and is currently pursuing his PhD from National Institute of Technology Agartala (NITA). He has served in various administrative positions at TCEA, including head of the Department, was in charge of Student Affairs, manager of Event Committee, member of the purchase committee. He was associated with variuos education institutes, including Convolution Educare, Techno International New Town (Formerly known as Techno India College of Technology) as an assistant professor. He is also a freelance software developer and project consultant of the software firm craftechgroup. He is an active associate member and junior convener of Computer Science and Engineering Div. of the Institution of Engineers (India) (IEI), Tripura State Centre. He has published different journals and participated in various national and international conferences. His current research interests include machine learning, artificial intelligence, time series prediction, digital image processing and MCDM.

Bhattacharya Diptendu
https://orcid.org/0000-0002-8981-710X

D. Bhattacharya received the BE degree from Malaviya National Institute of Technology (formerly MREC), Jaipur, Rajasthan, India, in 1988, also the M.E.Tel.E. (computer engineering) and PhD in engineering from Jadavpur University, Kolkata, India, in 1999 and 2016, respectively. He is currently an associate professor in the Department of Computer Science and Engineering, National Institute of Technology Agartala, India. He stood first class first position in order of merit in his M.E.Tel.E. Program. Dr. Bhattacharya was the head of the Department of Computer Science and Engineering, in National Institute of Technology, Agartala. He was also the winner of Weekly Nifty Prediction Contest organized by Personal Wealth Management Solutions Pvt. Ltd., Kolkata, India, in 2013. He has supervised 5 PhD theses. He has over 60 publications in international journals and conference proceedings. He is the author of book Time-Series Prediction and Applications: A Machine Intelligence Approach, Springer, 2017. Dr. Bhattacharya also authored 7 book chapters. He is popular among his students. Also, he supervised several B. Tech. and M. Tech. theses in his 32 years of teaching period in NIT, Agartala, India. His current research interests include artificial intelligence and soft computing, machine learning, IoT, optimization and precision agriculture, time-series prediction, computer vision, imaging, medical imaging, digital image and video processing, intelligent transportation system, intelligent vehicle, driving assistance system, online document image processing, natural language processing, pattern recognition etc.

Chatterjee Indranath
https://orcid.org/0000-0001-9242-8888

I. Chatterjee is working as a professor in the Department of Computer Engineering at Tongmyong University, Busan, South Korea. He received his PhD in computational neuroscience from the Department of Computer Science, University of Delhi, Delhi, India. His research areas include computational neuroscience, schizophrenia, medical imaging, fMRI, and machine learning. He has authored and edited 8 books on computer science and neuroscience published by renowned international publishers. To date, he has published numerous research papers in international journals and conferences. He is a recipient of various global awards on neuroscience. He is currently serving as a chief section editor of a few renowned international journals and serving as a member of the advisory board and editorial board of various international journals and Open-Science organizations worldwide. He is presently working on several projects of government & non-government organizations as PI/co-PI, related to medical imaging and machine learning for a broader societal impact, in collaboration with several universities globally. He is an active professional member of the Association of Computing Machinery (ACM, USA), Organization of Human Brain Mapping (OHBM, USA), Federations of European Neuroscience Society (FENS, Belgium), Association for Clinical Neurology and Mental Health (ACNM, India), and International Neuroinformatics Coordinating Facility (INCF, Sweden).

Chatterjee Prasenjit
https://orcid.org/0000-0002-7994-4252
p.chatterjee@mckvie.edu.in

P. Chatterjee is currently the dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has authored over 120 research papers in various international journals and peer reviewed conferences. He has authored and edited more than 20 books on intelligent decision-making, supply chain management, optimization techniques, risk and sustainability modelling. He has received numerous awards including Best Track Paper Award, Outstanding Reviewer Award, Best Paper Award, Outstanding Researcher Award and University Gold Medal. Dr. Chatterjee is the editor-in-chief of Journal of Decision Analytics and Intelligent Computing. He has also been the guest editor of several special issues in different SCIE / Scopus / ESCI (Clarivate Analytics) indexed journals. He is the lead series editor of Disruptive Technologies and Digital Transformations for Society 5.0, Springer. He is also the lead series editor of “Smart and Intelligent Computing in Engineering”, Chapman and Hall / CRC Press, Founder and Lead Series Editor of “Concise Introductions to AI and Data Science”, Scrivener – Wiley; AAP Research Notes on Optimization and Decision Making Theories; Frontiers of Mechanical and Industrial Engineering, Apple Academic Press, co-published with CRC Press, Taylor and Francis Group and “River Publishers Series in Industrial Manufacturing and Systems Engineering”. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).

Zavadskas Edmundas Kazimieras
https://orcid.org/0000-0002-3201-949X

E. Zavadskas is a professor, head of the Department of Construction Technology and Management at Vilnius Gediminas Technical University, Vilnius, Lithuania, and a chief researcher at Research Institute of Smart Building Technologies. Prof. E. Zavadskas is a renowned developer of numerous MCDM techniques and his research interests include application of MCDM tools in solving engineering and management decision making problems, mainly in civil and structural engineering area.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2023 Vilnius University
Open access article under the CC BY license.

Keywords
intuitionistic fuzzy set cross entropy measure consensus based WASPAS OSS-LMS package selection

Metrics
since January 2020
323

Article info
views

 171

Full article
views
203

PDF
downloads

 37

XML
downloads


Share


RSS