A Game-Theoretic Approach to Energy-Efficient Elevator Scheduling in Smart Buildings

Maleki, Erfan Farhangi; Bhatta, Dixit; Mashayekhy, Lena

doi:10.1109/tsmc.2023.3237027

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…The most recently published articles from the year 2023 presented five studies related to engineering and transportation [39][40][41][42][43]. Shi and Zhu [39] proposed a dual-phase control strategy for cargo transportation using a partial space elevator, which effectively balances multiple control objectives and achieves a stable equilibrium state.…”

Section: Related Literaturementioning

confidence: 99%

“…Gu et al [42] analyzed the seismic response of a 26-story-frame shear wall structure before and after the installation of elevators and optimized the design scheme. Maleki et al [43] proposed a method to predict the risk of passengers being trapped in elevators during earthquakes, which could help with virtual drills and emergency plans. Finally, a further study proposed an efficient approach for elevator scheduling in smart buildings to reduce energy usage and enhance user experience, resulting in significant improvements in energy consumption and service time.…”

Section: Related Literaturementioning

confidence: 99%

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Minimizing Energy Usage and Makespan of Elevator Operation in Rush Hour Using Multi-Objective Variable Neighborhood Strategy Adaptive Search with a Mobile Application

2023

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The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.

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