Dealing with value constraints in decision making using MCDM methods

In our daily life, we are always facing decision making problems with different complexities and requirements. Therefore, the need to design new theories, methods and tools to solve these kinds of problems, as efficiently as possible, becomes a real challenge. In the current research, we deal with decision problems wherein value constraints are expressed on the performance ratings of the alternatives. We focus on MCDM (Multi-Criteria Decision Making) methods to solve such problems. When value constraints are specified, traditional MCDM methods proceed upstream by removing from the research space all the non-satisfactory alternatives, while more recent approaches provide a subjective ranking of these alternatives. To overcome some of these issues, we have introduced, recently, a new MCDM method called, ISOCOV (Ideal Solution with Constraint on Values). ISOCOV aims at providing to the decision maker a more accurate solution while dealing with the given value constraints. We propose in this paper to introduce more flexibility in this method and study its accuracy by comparing its rendering with other methods through its application on a real dataset. The adaptation introduced in our study makes it possible to specify the problem by assuming the value constraints either as mandatory (hard constraints), or as non-compulsory (soft constraints). In the first case, the alternatives that are meeting all the constraints are ranked on the top, whereas the rest of the field is ranked below. However, if the constraints are soft, ISOCOV ranks the alternatives according to the combination of their performance ratings and their closeness to meet all the constraints. (C) 2020 Elsevier B.V. All rights reserved.