Mathematical Methods for Decision Making and Optimization

Dear Colleagues,

Numerous real-world problems in science, engineering, industry, economics, and business are solved using sets of mathematical concepts and techniques called optimization and decision making. These fields are extremely rich as a result of the creation of numerous novel algorithms. The quantitative measurement of quality has become the most effective foundation for developing and enhancing policies over the last few years. Mathematical techniques, statistical analysis, and optimization techniques can all improve decision-making processes. A number of new challenges have recently emerged in the fields of mathematical modeling due to the development of new techniques and algorithms based on artificial intelligence, machine learning, neural networks, fuzzy theory, evolutionary computation, multi-objective optimization, multi-criteria decision making, rough set theory, and data analytics, to name a few. The decision-making process can be carried out by employing numerous methods and tools, as well as using diverse objectives, in order to provide an acceptable solution to the new problems encountered. The formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex in real-world issues, and a variety of techniques and methodologies are used to reduce risks or raise the quality of decisions being made concurrently.

The goal of this Special Issue is to collect the most recent developments in mathematical decision-making and optimization techniques for a variety of engineering, science, and management applications. Both original research pieces and review papers are welcome to be submitted to this Special Issue.

Topics include, but are not limited to:

• Application of AI, ML, fuzzy and other techniques;
• Data mining and statistical learning;
• Decision support systems;
• Design optimization;
• Evolutionary algorithm;
• Fuzzy computing;
• High-performance computing;
• Intelligent computing;
• Mathematical modeling;
• Machine learning applications;
• Multi-objective programming;
• Multi-criteria decision making;
• Metaheuristic algorithms predictive modeling and analytics; 
• Rough set theory;
• Neural networks.

Dr. Prasenjit Chatterjee
Guest Editor

Manuscript Submission Information

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