journal article·2022Open access

Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker

Hanan Abdullah Mengash, Manel Farouk Ayadi

Computers, Materials & Continua· Volume 70· Issue 2

Abstract

Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select the best choice. We utilize the positional advantage operator concept to develop regret and rejoice mathematical equations, and prove them. The proposed MCDM procedure that incorporates these two emotional factors offers a decision-maker the flexibility to trade off some benefits in order to gain a state of psychological satisfaction. More specifically, regret and rejoice are presented mathematically to enable the decision-maker to determine the values of regret and rejoice, and then make the decision in which the rejoice value is higher than the regret value. To test the performance of this new procedure, we apply it to three numerical examples proposed in previous works. The results are matched with those obtained by other methods such as the regret model, VIKOR, PROMETHEE I, and PROMETHEE II, thereby proving the efficacy of the new procedure.
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Research context

OpenAlex topics: Multi-Criteria Decision Making · Decision-Making and Behavioral Economics · Forecasting Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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