Decision-making is a fundamental activity in various fields, including economics, engineering, management, and more. In many cases, the decision maker faces conflicting objectives, uncertain information, and incomplete knowledge. Multi-criteria decision-making (MCDM) is the decision-making process about alternatives with more than one objective. More than one objective can be considered, but this does not mean that all of them have equal weight. Recent research has shown that many Fuzzy Optimization Techniques (FOTs) are more robust than traditional optimization under uncertainty models; however, fuzziness limitations have prevented these methods from dealing directly with MCDM problems in a rigorous way. This calls for advanced techniques to handle such complexity and provide a satisfactory solution.

Multi-criteria decision-making (MCDA) is concerned with problems in which several alternatives are evaluated based on the satisfaction of multiple criteria. The use of fuzzy logic in multi-criteria decision-making has gained popularity, and some interesting results have been obtained. The approximation of the nonlinear objective function by a linear function, associated with the primary goal of fuzzy optimization techniques, does not always lead to efficient solutions; in fact, sometimes, it may even lead to infeasible solutions. Multi-criteria decision-making (MCDM) has emerged as one of the most popular fields in engineering and operations research because it has many real-world applications. However, most studies on this topic have been conducted under certain conditions, and the results obtained might only apply to those conditions. Therefore, the need for further research on MCDM under uncertainty using fuzzy optimization techniques is strongly felt to generate additional knowledge about ambiguous data approximations for model building, model assessment, and model-based decision-making under uncertainty conditions. The concept of decision under uncertainty has found a very important place in the present literature on optimization and its techniques. Uncertainty is an attribute of reality over which we have no control but only to react to it. It can be due to such factors as ignorance about the nature of a problem or its parameters, unpredictability of future outcomes, and inaccessibility of accurate information required for making decisions. Fuzzy optimization techniques offer a promising approach for multi-criteria decision-making under uncertainty. These techniques use fuzzy logic to represent and manipulate uncertain and imprecise information and optimize the decision according to criteria. Fuzzy optimization has been successfully applied in various fields, including finance, supply chain, and environmental management. The goal of this special issue is to provide a comprehensive overview of the state-of-the-art fuzzy optimization for multi-criteria decision-making under uncertainty.

Topics of interest include, but are not limited to:

• Recent advances in the field of fuzzy optimization for multi-criteria decision-making under uncertainty
• Recent advances in the field, especially on modeling and representation issues, computational methods, and applications
• Fuzzy optimization techniques for multi-criteria decision-making under uncertainty
• Applications of fuzzy optimization in real-world problems
• Application of fuzzy logic and soft computing techniques to multi-objective decision-making under uncertainty.
• Fuzzy nonlinear programming with applications to finance and materials science
• Applications of fuzzy optimization for multi-criteria decision-making under uncertainty
• Combination of different approaches to fuzzy optimization for multi-criteria decision-making under uncertainty
• Potential applications of fuzzy optimization in multi-criteria decision-making under uncertainty
• Advantages and limitations of fuzzy optimization compared to other approaches, such as traditional optimization and artificial intelligence techniques.
• Open research problems and future directions in fuzzy optimization for decision-making under uncertainty.

Guest Editor Details:

Dr. Muhammad Sulaiman
Associate Professor
Department of Mathematics,Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, Pakistan.
E-Mail: msulaiman@awkum.edu.pk , si@msulaiman.org
Personal Webpage: https://www.msulaiman.org/  
Google Scholar: https://scholar.google.com/citations?user=Ikq95ogAAAAJ&hl=en  
Research Gate: https://www.researchgate.net/profile/Muhammad-Sulaiman-23   
Biography: Muhammad Sulaiman received his B.Sc. degree from the University of Peshawar in 2004, M.Sc., and M. Phil degrees in mathematics from the Quaid-e-Azam University Islamabad Pakistan in 2007 and 2009 respectively. He received his Ph.D degree in mathematics from the University of Essex UK in April 2015. He is working as an Associate Professor of Mathematics at Abdul Wali Khan University Mardan, Pakistan. His area of interest includes Heuristics, Optimization, Applied Mathematics, Artificial Intelligence, Algorithms, Combinatorial Optimization, Scheduling, Simulation, Optimization Modeling and Mathematical Programming. His Publications are around 83 and his citations are around 1200 with a h- index of 21.

Dr. Maharani Abu Bakar
Special Interest Group Modelling and Data Analytics, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, Malaysia.
E-Mail: maharani@umt.edu.my  
Scholar Google: https://scholar.google.com/citations?user=XnGJLUIAAAAJ&hl=en   
Research Gate: https://www.researchgate.net/profile/Maharani-Abu-Bakar 
Biography: Maharani Abu Bakar received her PhD in Mathematical Sciences in 2015 at University of Essex, Colchester and completed her degree in MSc Mathematics in 2005 at Gadjah Mada University, Yogyakarta. She is working as a Lecturer in school of Informatics and Applied Mathematics. Her area of interest includes Differential Equations, Numerical Modeling, Numerical Mathematics, Mathematical Computing, Numerical Analysis, Applied Mathematics, Algebra, Engineering, Applied and Computational Mathematics and Mathematical Modelling. Her Publications are around 30 and her citations are around 173 with a h- index of 7.

Dr. Zardad Khan
Assistant Professor
Department of Analytics in the Digital Era, United Arab Emirates University, Al Ain, United Arab Emirates.
Mail ID: zaar@uaeu.ac.ae  
Profile Page: https://www.uaeu.ac.ae/en/cbe/profile.shtml?email=zaar@uaeu.ac.ae  
Scholar Google: https://scholar.google.co.uk/citations?user=qOgSjRUAAAAJ&hl=en  
IEEE: https://ieeexplore.ieee.org/author/37086873739  
Biography: Zardad Khan received his master’s degree in statistics with distinction (Gold Medal) from the University of Peshawar, Pakistan in 2008, graduated MPhil in Statistics from Quaid-i-Azam University Islamabad, Pakistan in 2011 and received PhD degree in Statistics from the University of Essex, United Kingdom in 2015. He has worked as an Associate Professor of Statistics at Abdul Wali Khan University Mardan, Pakistan. He has also done a 1year post-doctorate from the University of Essex, UK. During his postdoc, he has worked under the Knowledge Transfer Partnership (KTP) project to develop and use machine learning methods for the Essex and Suffolk Country councils and Profusion (London) for various data analysis problems. Zardad’s focus is on data science and machine learning working on developing algorithms and R programming packages for classification problems and feature selection for high dimensional data. His other research interest are computational statistics, survival analysis and biostatistics.