Directional optimization of elevator scheduling algorithms in complex traffic patterns

Abstract

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements.