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Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems

Author

Listed:
  • Yerzhigit Bapin

    (School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan)

  • Kanat Alimanov

    (School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan)

  • Vasilios Zarikas

    (School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
    General Department, University of Thessaly, 38221 Volos, Greece)

Abstract

A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively.

Suggested Citation

  • Yerzhigit Bapin & Kanat Alimanov & Vasilios Zarikas, 2020. "Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems," Energies, MDPI, vol. 13(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6161-:d:450065
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    References listed on IDEAS

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    1. Amrin, Andas & Zarikas, Vasileios & Spitas, Christos, 2018. "Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 211-225.
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    Cited by:

    1. Mark B. Luther & Igor Martek & Mehdi Amirkhani & Gerhard Zucker, 2022. "Special Issue “Environmental Technology Applications in the Retrofitting of Residential Buildings”," Energies, MDPI, vol. 15(16), pages 1-4, August.
    2. Surajet Khonjun & Rapeepan Pitakaso & Kanchana Sethanan & Natthapong Nanthasamroeng & Kiatisak Pranet & Chutchai Kaewta & Ponglert Sangkaphet, 2022. "Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention," Sustainability, MDPI, vol. 14(5), pages 1-19, February.

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