Mathematical models of multi-criteria decision-making (MCDM) are applied in almost all scientific fields and disciplines. In the early days of resolving MCDM problems, the aim was to create methods for specific tasks, however, the essential problem of translating the real state into the mathematical model was also recognized very quickly. More often than not, a part of the data that exists in the real problem cannot be translated in a simple manner into the mathematical model. In reality, some elements affect the decision but cannot be quantified and presented through the standard units of measure. Decision-making often depends on the decision-makers' perception (experts), education, experience, and other similar parameters. In addition, decision-making models are often based on elements that lack complete information, meaning that decision-makers must estimate or assess the real environment.

All these aspects have significantly affected development by increasing the number of fields that deal with uncertainty, with the primary goal of quantifying people's perception of phenomena in the best possible way. Therefore, through their research, scientists increasingly modify standard MCDM methods by applying solutions from fields that handle uncertainties successfully. Although the development of fields and tools that handle uncertainty has reached a high level of maturity, researchers are still searching for methods that would enable us to map the state of reality into a mathematical model of MCDM unambiguously. Therefore, decision-making in an uncertain environment is of interest to a large number of scientists.

The aim of this Special Issue is to encourage and support scientists to make another step forward in the development of MCDM modeling in uncertain environments, and we hope to incorporate recent developments in the field of applied science and engineering. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Theoretical foundations of MCDM with uncertainty
• Fuzzy sets and their applications in MCDM
• Rough sets and their applications in MCDM
• Grey numbers and their applications in MCDM
• Decision-making applications employing soft computing
• Neutrosophic sets and their applications in MCDM
• D numbers and their applications in MCDM
• Aggregation operators and their applications in MCDM
• Mathematical programming in MCDM under uncertainty
• New trends in multi-criteria decision-making