Uncertainty Analysis, Decision Making and Optimization

Dear Colleagues,

Decision making usually takes place under uncertainty. Uncertainty analysis in optimization means studying the problems on how to deal with uncertainty efficiently, which is a crossing field between optimization theory and uncertainty theory. In fact, many realistic optimization problems can be solved in the presence of uncertainty. The aim of this Special Issue is to attract leading researchers in these areas in order to reflect the recent advances in uncertainty analysis and optimization theory, both from a theoretical and an applied point of view. All articles related to uncertainty analysis and optimization are invited to be submitted for this Special Issue.

You are cordially invited to submit papers related to all aspects of uncertainty analysis and optimization, both theoretical and practical. The topics of interest for this Special Issue include (but are not limited to): uncertainty analysis on the background of big data, theoretical foundations of optimization with big data, uncertainty modeling, optimization modeling, multi-criteria optimization, intelligent computing, uncertainty theory, uncertain programming, uncertain graphs and uncertain hypergraphs, uncertain network optimization, uncertain hypernetwork optimization, logistics network optimization, big data analysis, uncertain system analysis, uncertain risk analysis, uncertain reliability analysis, uncertain statistics analysis, complex networks and social networking analysis, uncertain processes, uncertain calculus, uncertain differential equations, uncertain VRP, and so on. 

Prof. Dr. Jin Peng
Prof. Dr. Dan Ralescu
Guest Editors

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