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Updated: Jan 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
Queuing-theory-driven renewable energy investment decisions through artificial expert choices with dynamic hybrid
Gang Kou1,2, Serhat Yüksel3,4,5, Hasan Dinçer3,4,5
1School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China.
Abstract:
This study examines queuing dynamics in peer-to-peer energy trading systems by integrating Q-learning assessment with hybrid molecular fuzzy modeling. Using large expert datasets, the framework evaluates decision-making strategies that influence energy allocation, efficiency, and market stability. The approach captures the interplay between adaptive learning and uncertainty, offering insights into how decentralized participants manage trading behavior under varying demand and supply conditions. Results demonstrate that the proposed modeling system effectively identifies patterns of cooperation, competition, and equilibrium formation in blockchain-enabled trading environments. By framing queuing as both a technical and behavioral process, the analysis connects individual choices with system-level performance. Beyond methodological contributions, the findings underscore the relevance of intelligent computational tools for addressing challenges in sustainable energy distribution. This work highlights the broader significance of combining artificial intelligence, fuzzy systems, and decentralized market mechanisms to advance resilient and efficient resource management.
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