Fuzzy TOPSIS Case Study

Fuzzy TOPSIS has been used frequently in many research for decision making on multi-criteria decision making. One of the most used subject where the TOPSIS has been frequently used to calculate it best alternative is in e-learning. E-learning is one of the e-service application that manage diverse type of electronic to deliver vocational education and training. E-learning has become an important part in education industry. E-learning provided full access to student without having them to attend the classroom. The usage of e-learning has become mandatory for many education institution and industry. From the e-learning service provider perspective, the internet act as intermediary between the e-learner and the e-learning websites. Since the e-learning
Many big organization has invest such a big money and time to evaluate it. There also many researcher that has done multiple study on e-learning such as the study that has been done by Dimpal Jain, Rakesh Garg and Ashu Banshal. They has presents a parameterized selection and evaluation of e-learning websites using TOPSIS method. . They use a total of 21 e-learning websites for their sample. The aim of this work is to represent the problem of selection and evaluation of e-learning websites as multi-criteria decision making. From the multi-criteria decision making result, then they applied Fuzzy TOPSIS to ranking the e-learning web sites. From the method applied in this study the alternative that have value nearest to the one will be ranked first and the other way
Mario Fedrizzi and Andrea Molinari in their paper has shown Fuzzy TOPSIS used by a panel of expert to evaluate of e-learning paths. In e-learning setting, it is important to evaluate the different alternative on the paths’ proposal because of the attention given to the construction of Learning Objects that available from in Learning Management System. To support their evaluation process they has present a model. The research on the evaluation of alternatives of e-learning paths is carried out by a panel of expert by using fuzzy TOPSIS method. They use the method under the assumption that the scores are linguistically assessed and represented by triangular fuzzy number. From the individual ranking of alternative, they introduced a consensus modelling mechanism where the disagreement between the rankings of single experts and the group ranking. They measured the disagreement with a Spearman foot rule distance and from there they propose a compromise solution which is determined using a constrained optimization model. However this has shown the weakness of fuzzy TOPSIS because the result between the individual experts is not same so they need to implement other additional method to make a compromise