A Comparative Study of TOPSIS Method via Different Distance Measure

Abstract

One of the techniques used to support strong decision making in domains where selection of best alternative is highly complex is the Fuzzy TOPSIS method. In TOPSIS procedure different distance measures are used to calculate the distance of each fuzzy number from both Fuzzy Positive Ideal Solution (FPIS) and Fuzzy negative Ideal Solution (FNIS). In this paper, six well known distance measures have been taken into account which are available in literature and applied to TOPSIS procedure to perform a comparative study throughout numerical problems. The main purpose is to compare the stability and reliability of TOPSIS method in decision science through different distance measures. Fuzzy triangular number is used in our entire case study.

Novel intelligent TOPSIS variant to rank regions for disaster preparedness

“…HCF-TOPSIS method leverages two sets namely hypothetically best (𝑃 + ) and worst (𝑃 − ) solution set [29] to find the optimal solution which is not only nearest to the best solution, but also the farthest from the worst solution [10]. Observe that 𝑃 + is a supposed solution having the values of sub-criteria that correspond to positive ideal (best) criteria values in [𝑉].…”

“…Use of absolute distance measures keeps RRP under control [32]. Using relative distance measure, computations of positive and negative ideal solutions result in non-uniformities in rankings given by TOPSIS [29]. Normalization procedure which uses absolute terms reduces RRP as it allows the alternatives to remain independent to some extent Talukdar and Dutta [29].…”

“…Using relative distance measure, computations of positive and negative ideal solutions result in non-uniformities in rankings given by TOPSIS [29]. Normalization procedure which uses absolute terms reduces RRP as it allows the alternatives to remain independent to some extent Talukdar and Dutta [29]. tackles RRP by considering the TOPSIS value of alternatives based on absolute closeness to positive and negative ideal solution [33].…”

A Novel Framework to Strengthen Early Warning Systems

“…Subsequently, the weights (resilience) of the sub-criteria are multiplied with $"% to obtain the performance of each region DM $(% with respect to the overall performance of DIP on each sub-criterion. Phase II computes two separation vectors namely a hypothetically best () * ) and worst () ) solution set [25] to identify the best solution which is not only closest to the best possible solution, but also the farthest from the worst possible solution [20]. In order to get the hypothetical best solution ) * , the maximum value for the benefit criterion ( * and the minimum value for the cost criterion ( from regions are considered.…”

“…Further, separation vectors ) * and ) are obtained for each region # from its corresponding positive and negative ideal solutions respectively. Euclidean distance is used for the calculation of the separation vectors due to its popularity and simplicity over other distance measures [25,26]. The computed separation vectors for each region # are used in phase III, to decide their ranking based on their distance from the optimized ideal reference point +.…”