The need for application uncertainty models has manifested in the fact that a large number of complex problems include more conflict factors. So developing such models which have the ability to balance conflicting goals where there are many alternative courses of action available to the decision-makers when resources, time, and funds are limited is a large challenge. Uncertainty models, including fuzzy set theory, neutrosophic theory, rough set theory, grey theory, Dempster-Shafer evidence theory etc., have everyday applications in decision making about the problems in complex systems. In the last few years, applications of uncertainty models have been extended to solve real-world problems and other disciplines.

Complex problems seek the application of scientific principles for practical objectives such as the processes, manufacture, design, and operation of products while accounting for constraints involving uncertainty factors. Various factors need to be considered in order to address complex problems, which are critical for the overall sustainability of human development and activity.

This Special Issue aims to collect papers that develop uncertainty models (simulation, optimization, probability and statistics, decision theory, systems dynamics, multi-criteria decision making, etc.) in the field of complex systems. Review articles that deal constructively with complex problems debates are also welcome.

Potential topics include but are not limited to the following:

• Applications of uncertainty models as support in creating innovation
• Hybrid decision analysis for complex problems
• Innovative applications of uncertainty models
• Integration of novel uncertainty models
• Mathematical programming for complex problems
• Multi-criteria decision making (MCDM) under uncertainty
• Sustainable engineering decision-making with uncertainty
• Uncertainty models for risk analysis
• Uncertainty models for solving complex facility location problems
• Uncertainty models for solving complex logistics processes
• Uncertainty models for solving complex routing and/or scheduling problems
• Uncertainty models for the efficiency analysis and/or performance measurement