| Abstract |
This paper explores the innovative intersection of queueing theory and artificial intelligence (AI), addressing emerging challenges for Smart City Telecommunication System. We propose a novel approach that integrates queueing theory with AI methodologies, particularly artificial neural networks (ANNs), to optimize strategic parameters in Markov Decision Process (MDP) problems in communication systems. These parameters include server numbers, customer waiting times, queue lengths, and other critical metrics. Our fusion approach demonstrates high efficacy and indicates significant advancements in applying machine learning to complex queueing theory issues. The study underscores the practical applications of this integration across various domains. We provide real-world examples illustrating the strategic use of AI-enhanced queueing models in improving user experiences and optimizing system efficiencies. Our discussions cover the benefits of intelligent resource allocation, dynamic load balancing, and adaptive service time optimization. We extend our exploration through specific case studies demonstrating the efficacy of this integration in industries such as telecommunications, healthcare, and transportation. For instance, in telecommunications, AI-driven queue management systems can dynamically allocate bandwidth to ensure optimal service quality. In healthcare, queueing models enhanced by AI can improve patient flow and reduce wait times, potentially leading to better healthcare outcomes. In this paper, we investigate the application of deep learning models to queueing theory, specifically focusing on [mention specific problem or application, e.g., 'forecasting queue lengths in real-time systems']. We implement and evaluate several deep learning architectures to determine their effectiveness in modeling and predicting queue dynamics. The paper concludes by emphasizing the necessity for continued interdisciplinary research, encouraging collaboration between AI experts and queueing theorists. This synergy is essential for unlocking new potential and addressing the increasingly complex challenges posed by modern systems. By fostering such collaboration, we anticipate significant breakthroughs that will transform both fields, leading to more efficient, adaptive, and intelligent systems capable of meeting future demands across various sectors. © 2024 IEEE. |
