Decision-making (DM) problems under uncertain and vague information are a big challenge for researchers. Computational intelligent techniques (e.g., neural networks, fuzzy systems, neuro-fuzzy systems, and evolutionary algorithms) have been successfully applied for many engineering problems. Computational intelligence has been applied in terms of theory, design. Within this field, there are emerging trends such as granular computing, complex systems, information fusion, mechatronics, and bioinformatics.

Computational intelligence shows its usefulness and has been often emphasized by Professor Lotfi Zadeh, the inventor of fuzzy logic. Fuzzy logic is a fundamental and well-studied approach for dealing with vague, uncertain, ambiguous, and imprecise information. This is due to the fuzzy logic's suitability for explaining uncertainties and coping with various DM problems. As a result, fuzzy logic has been commonly implemented to deal with a significant fraction of incomplete knowledge in various situations and contexts. There have been many popular applications and implementations of fuzzy logic in the DM over the years. The DM has been one of the branches in which fuzzy logic has found widespread use.

The aim of this Special Issue is to bring together original research and review articles discussing computational intelligence and fuzzy modelling for sustainable decision-making in engineering. We welcome submissions including optimization, prediction, and modelling.

Potential topics include but are not limited to the following:

• Application of computational intelligence in engineering innovations
• Uncertain multi-criteria decision-making for environmentally friendly engineering processes
• Fuzzy modelling and engineering applications
• Evolutionary computation for sustainable engineering
• Artificial intelligence for sustainable engineering
• Uncertainty modelling for sustainable logistic processes
• Ecological engineering decision-making
• Multi-criteria decision-making (MCDM) optimization in sustainable engineering
• Information aggregation in sustainable engineering
• Information measures in sustainable engineering
• Computational intelligence for solving sustainable facility location problems
• Computational intelligence for solving sustainable routing and/or scheduling problems
• Computational Intelligence for efficiency analysis and/or performance measurement
• Computational intelligence for the development of sustainable investment plans in engineering
• Application of multi-criteria approaches in engineering under uncertainty