Intelligent Agent-Controlled Elevator System: Algorithm and Efficiency Optimization
The study introduces an innovative intelligent agent-controlled elevator system specially designed to improve passenger wait times and enhance the efficiency of high-rise buildings. By utilizing the classic single-agent planning model, we developed a unique strategy for handling calls from halls and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Russian Academy of Sciences, St. Petersburg Federal Research Center
2025-01-01
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| Series: | Информатика и автоматизация |
| Subjects: | |
| Online Access: | https://ia.spcras.ru/index.php/sp/article/view/16482 |
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| Summary: | The study introduces an innovative intelligent agent-controlled elevator system specially designed to improve passenger wait times and enhance the efficiency of high-rise buildings. By utilizing the classic single-agent planning model, we developed a unique strategy for handling calls from halls and cars, and combined with this strategy we significantly improved the overall performance of the elevator system. Our intelligent control methods are in-depth compared with conventional elevator systems, assessing three important performance indicators: response time, system capacity to handle multiple active elevator cars simultaneously, and average passenger waiting time. The results of the full simulation show that an intelligent agent-based model consistently exceeds conventional elevator systems in all measured criteria. Intelligent control systems have significantly reduced response times, and improved simultaneous elevator management and passenger wait times, especially during high traffic. These advances not only improved traffic flow efficiency, but also greatly contributed to passenger satisfaction and brought smoother and more reliable transport experiences within the building. Furthermore, the increased efficiency of our systems is in line with the goals of building energy management, as it minimizes unnecessary movements and idle time. The results demonstrate the system's ability to meet dynamic, high-occupation environment requirements and mark a significant step forward in intelligent infrastructure management. |
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| ISSN: | 2713-3192 2713-3206 |