Multicriteria Decision Making Using TOPSIS Method: An Accurate Selection of Deserving Candidates

In intelligent decision support systems, multi-criteria decision-making techniques have widely been used for various kinds of executive decisions. Under the United Nation poverty alleviation programs, Non-Governmental (NGO), Non-Profitable Organizations (NPO) and privileged people use survey methods to collect deserving people’s data for charitable donations, for example financial aid etc. They use manual or semi-automatic data collection methods and then consider very few criteria to finalize the list of deserving donees for the aid in hand. This normally results in a list including donees who either don’t deserve or if so, they are not the right candidates. In addition to this, final recommendations of the NGOs and NPOs don’t address poverty gap of the donees and place them all at the same level which results in unfair distribution of the charity. This makes the problem as a complex decision-making process due to the consideration of multiple criteria at a time. To address these issues, this paper formulates the problem of deserving donees selection as a multi-criteria decision-making problem. A novel multi-criteria decision-making methodology, integrating Analytical Hierarchy Process (AHP) with Technique for Order Preference by Similarity to Ideal Solution(TOPSIS), is proposed to rank the candidates list according to their intensity of poverty gap. To realize the proposed methodology, a set of 15 unique criteria are identified and data is collected from 212 subjects, which are then ranked accordingly. The results show that the methodology is assistive to managements of NPOs and privileged individuals to accurately rank donees and consequently disburse aids in most deserving families.