## 1 Introduction
Another study [5] examines industrial elevator systems, prevalent in daily use, operating within uncertain environments influenced by factors such as unpredictable passenger traffic, uncertain passenger attributes and behaviors, and hardware delays, aiming to evaluate and enhance system robustness in addressing uncertainties for improved design and implementation strategies. These research endeavors [5; 6] predominantly center on the regulation and governance of elevator systems, delving deeply into their internal mechanisms, encompassing both software and hardware components. However, these studies have yet to comprehensively examine additional parameters, such as the influence and impact of passengers utilizing these elevator systems. In [6], the authors introduce an elevator traffic pattern recognition method using the density peak clustering algorithm, using historical passenger flow data cluster analysis to identify the center coordinates of the traffic pattern and then using five-minute real-time passenger flow data to select cluster centers based on the highest density and distance from high-density points. In [7], a method is introduced for estimating elevator traffic flow, leveraging a hybrid long short-term memory neural network, which accounts for the hourly running frequency and incorporates trends, periodicity, and data fusion for accurate future flow estimation, addressing a gap in existing research focusing on real-time monitoring rather than future flow prediction. In both studies [6; 7], the authors aim to discern the traffic dynamics characterizing elevator utilization. Nevertheless, this pattern does not persist consistently throughout all intervals. While prominently observable during peak periods (morning office hours, lunchtime, and end-of-day departures), its manifestation diminishes notably during other timeframes. In [8], an algorithm is presented to solve the problem of facility layout across multiple floors, considering the capacity of the elevator along with the costs of material flow between departments, with the aim of optimizing the number, location, and allocation of elevators. However, without explicitly measuring or addressing passenger waiting time as a primary optimization metric, the proposed algorithm has uncertain effectiveness in reducing or optimizing waiting times. In [9], the authors focus on controlling and managing power in a hybrid system for elevators, featuring a photovoltaic generator, a battery bank, supercapacitors, and a permanent-magnet synchronous motor, employing a neural network trained with a frequency-based strategy to effectively manage load-demand splits. In [10], convolutional neural networks (CNNs) are utilized to develop a classifier model for recognizing and classifying floor numbers and commands spoken by elevator users, aiming to mitigate physical contact with elevator buttons and potentially reduce COVID-19 transmission. Both studies [9; 10] focus mainly on utilizing neural network applications to optimize elevator systems. However, there is a potential disadvantage regarding reducing passenger wait times due to the complexity of neural networks. Neural networks, while attempting to learn patterns and predict them, may face challenges in dynamically adapting to fluctuations in real-time passenger demand, traffic patterns, and unforeseen variables within the elevator environment. Their dependence on historical data and training may limit their ability to adapt quickly to sudden changes, potentially affecting their effectiveness in minimizing passenger wait times rapidly during dynamic operational scenarios. In [11], the authors introduce a genetic algorithm tailored for conventionally controlled double-deck elevators, showcasing superior performance compared to a tabu search algorithm across varied fitness evaluation functions for car-landing call allocation within the elevator group control system (EGCS).