Abstract
In most of real world problems, alternatives are evaluated according to a large set of criteria which confuses the Decision Maker (DM) in terms of allotting alternatives’ assessments. So, to reduce the complexity of the presented problem, it is recommended to organize the criteria into a hierarchy tree to decompose the main problem into sub-ones. Therefore, the DM gains detailed insight on each level of the hierarchy instead of focusing only on one level. In this context, we propose an extension of the Additive Ratio ASsessment (ARAS) method to the case of hierarchically structured criteria. The proposed approach is called Hierarchical Additive Ratio ASsessment (ARAS-H) method. A major advantage of the ARAS-H method is that it enables the DM to analyze the partial pre-orders (the rankings of the alternatives) at each node of the criteria tree i.e. according to each sub-criterion. The partial pre-orders present solutions of the problem with respect to each subset of criteria. In view of determining the criteria weights at each level of the hierarchy tree, we apply the AHP method. Finally, we apply the ARAS-H method on a case study related to tourism which aims to rank tourist destination websites brands in accordance with a four levels criteria hierarchy.
Highlights
Multiple Criteria Decision Aiding (MCDA) can be defined as a general framework for supporting complex decision-making situations with multiple and often conflicting objectives [17,20]
The International Society on Multiple Criteria Decision Making (MCDM) defines it as “The study of methods and procedures by which multiple and conflicting criteria can be incorporated into the decision process”
The last stage of the Additive Ratio ASsessment (ARAS)-H algorithm corresponds to the calculation of the utility degrees of the alternatives to rank them in a decreasing order at the first level of the hierarchy tree
Summary
MCDA can be defined as a general framework for supporting complex decision-making situations with multiple and often conflicting objectives [17,20]. As a matter of fact, most of Multiple Criteria Decision Aiding (MCDA) methods deal with a flat structure of criteria. For ranking problems, very few methods in the MCDA literature used a hierarchy of criteria for the decomposition of decision problems. The hierarchical structure decomposes the primary objective into separate components analyzed on their turn into sub-dimensions toward the lowest level of the hierarchy tree. Structuring decision problems is needed in circumstances requiring a large set of criteria depicting the examined problem In this way, employing a hierarchical decomposition facilitates the interpretation of the results as it permits DMs to explore feasible elementary dimensions of the whole problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
