Advanced Intelligent Algorithms for Decision Making under Uncertainty

Dear Colleagues,

In the ever-evolving landscape of technology and data, decision-making processes are increasingly confronted with uncertainty. The present Special issue “Advanced Intelligent Algorithms for Decision Making Under Uncertainty” aims to gather the most recent and notable studies in intelligent algorithms designed to navigate and optimize decision-making under uncertain conditions. These algorithms leverage machine learning, artificial intelligence, statistical methods, data analytics, numerical and optimization methods for large-scale problems, and computational techniques to provide robust solutions in diverse fields such as industry, finance, healthcare, engineering, and logistics.

The growing challenges and complexity of business the environment created difficulties in making management decisions related to the inability to efficiently and effectively predict the business results of economic processes. An environment with a high intensity of changes determining decisions, taken in uncertain situations caused by unpredictability in the behavior of competition, political instability, inflation and demographic collapse, uncertainty at the local, regional and global level. Management faces inaccurate limitations due to inability to sense and detect continuous changes in the influence of environmental factors. Management in the business environment with many instability and unreliability. Managers get into stressful situations, take decisions at risk, with scarce and inaccurate information, even forced by constant changing circumstances to decide even in conditions of absence of information. The idea is to stimulate the development of modern alternatives for optimal and efficient business management in an uncertain business environment.

The topics of interest include, but are not limited to:

• Intuitionistic fuzzy logic and its applications in decision-making problems.
• Development and application of intelligent algorithms in decision-making.
• Optimization techniques for uncertain decision problems
• Machine learning and AI methodologies enhancing decision models under uncertainty
• Probabilistic models and inference
• Predictive analytics and data-driven decision-making approaches
• Stochastic processes and simulations
• Theoretical models and computational techniques for decision support under uncertainty.
• Algorithmic strategies for improving decision efficiency and accuracy.
• Real-world applications of intelligent decision models in various sectors such as industry, healthcare, finance, engineering, and logistics.
• Computational algorithms for large-scale problems.

Dr. Stoyan Tranev
Dr. Velichka Traneva
Guest Editors

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• artificial intelligence
• decision support systems
• data analytics
• intelligent algorithms
• intuitionistic fuzzy logic
• large-scale problems
• machine learning
• numerical methods
• optimization
• predictive analytics
• probabilistic models
• stochastic processes
• uncertainty quantification