Mathematical models for decision-making are needed in a large number of scientific fields. A large number of published research in the field of multi-criteria decision making (MCDM) are an indicator of the importance of this enticing field of work. From the beginning of the development of this field, the problem relating to uncertainties in developing MCDM models has been clearly recognized. In order to address this issue, researchers have combined MCDM methods with other mathematical approaches that reduce the uncertainties in decision making in an acceptable manner.

The two most common approaches for this are fuzzy sets (fuzzy numbers) and rough sets (rough numbers). MCDM models are most often built as a set of predefined phases and steps, in which certain computations are realized. These computations become significantly more complex when the classical methodology is applied in a fuzzy or rough environment, which requires researchers to have profound knowledge of these areas. Previous research shows that uncertainties are handled equally well with either approach, while a few studies use a combined fuzzy-rough approach. Although significant improvements have been seen in these fields over the previous decade, both the fuzzy and rough approaches still have great potential for further development.

This Special Issue aims to gather research that addresses practical problems in applying the MCDM methods in a fuzzy, rough, or fuzzy-rough environment. In addition to solving practical problems, we welcome research submissions focusing on improving the MCDM methods by combining these with the fuzzy, rough, or fuzzy rough approach in various manners. This Special Issue will offer a platform for presenting carefully selected research and knowledge of theorists and practitioners, thus offering novelties in all three areas. Both original research and review papers are welcome.

Potential topics include but are not limited to the following:

• Neuro-fuzzy models for MCDM
• Fuzzy systems for MCDM
• Fuzzification of MCDM methods using classical fuzzy numbers
• Fuzzification of MCDM methods using interval fuzzy numbers
• Fuzzification of MCDM methods using intuitionistic fuzzy numbers
• Fuzzification of MCDM methods using hesitant fuzzy numbers
• Fuzzification of MCDM methods using q-rung orthopair fuzzy numbers
• Fuzzification of MCDM methods using Pythagorean fuzzy numbers
• Fuzzification of MCDM methods using spherical fuzzy numbers
• Modification of MCDM methods using rough numbers
• Modification of MCDM methods using interval rough number
• Modification of MCDM methods using a fuzzy-rough number