AZDP Elevator Control System: A Simulation Framework for Adaptive Zoning and Dynamic Prioritization

Abstract

This document outlines a Tkinter-based simulation framework for an Adaptive Zoning and Dynamic Prioritization (AZDP) elevator control system designed for high-rise, mixed-use buildings. The system integrates ENP system(view ) to optimize traffic flow, dynamically adjust elevator zones, and prioritize passenger requests using a weighted scoring system.

1. Introduction

Elevator systems in modern high-rise buildings face challenges such as minimizing wait times, optimizing resource allocation, and handling varying traffic patterns. The AZDP system addresses these by:

• Dynamic zoning: Clustering requests to assign elevators to specific zones.
• Dynamic prioritization: Calculating priority scores for requests based on waiting time, distance, elevator capacity, direction, and VIP status.

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2. System Architecture

The AZDP simulation models the following components:

• Building Model: Configurable number of floors (NUM_FLOORS) and mixed-use profiles.
• Elevator Model: Set of elevators (NUM_ELEVATORS) with capacity (ELEVATOR_CAPACITY).
• Traffic Generator: Simulates passenger requests based on time-of-day profiles and floor usage.
• Dynamic Zoning Algorithm: K-Means clustering using scikit-learn to assign elevator zones.
• Prioritization Engine: Calculates priority scores using weighted factors.
• Dispatching Logic: Assigns elevators using proximity, score, and zone association.
• Simulation Engine: Processes requests and moves elevators in discrete time steps.
• Tkinter GUI: Basic visualization of simulation progress.

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3. Algorithms

3.1 Traffic Simulation

Passenger requests are generated probabilistically using time-of-day (rush hour, lunch, etc.) and floor type (apartment vs. office).

3.2 Dynamic Zoning

Dynamic elevator zones are determined via K-Means clustering:

1. Data Collection: Historical request data (origin/destination floors) is collected.
2. Feature Extraction: Requests are represented as points in 2D space (origin and destination).
3. Clustering: Cluster data into NUM_ELEVATORS zones using K-Means.
4. Zone Assignment: Elevators are assigned to centroids of clusters.
5. Periodic Update: Zones update every ZONING_UPDATE_INTERVAL seconds.

3.3 Prioritization

Each request's priority score ($P$) is a weighted sum of factors: $P = w_t \cdot t + \frac{w_d}{d + 1} + w_c \cdot C + w_{dir} \cdot D + w_{vip} \cdot V$ where:

• $w_t, w_d, w_c, w_{dir}, w_{vip}$: Weights for waiting time, distance, capacity, direction, and VIP status.
• $t$: Waiting time (seconds).
• $d$: Distance to closest elevator (floors).
• $C$: Capacity factor (1 if not full, 0.5 if full).
• $D$: Direction factor (1.2 if elevator matches passenger direction, else 1).
• $V$: VIP status (1 if true).

The distance formula is: $d = \sqrt{(x_1 - x_2)^2 + (y_1 - y_2)^2}$

3.4 Dispatching

Requests are assigned elevators using priority scores, preferring same-zone elevators.

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4. Usage

• Clone the repository:

  git clone https://github.com/Ziqian-Huang0607/AZDP_Elevator_Algorithm
  cd AZDP_Elevator_Algorithm

Install dependencies:

pip install numpy scikit-learn matplotlib numba tkinter Configure parameters in Simulated_Building_Algorithm.py (e.g., NUM_FLOORS, ENP_weights).

Run the simulation: python Simulated_Building_Algorithm.py

6. Real-World Considerations Robustness: Handle outages and failures. Hardware Integration: Implement on microcontrollers with low-level code. Certification: Comply with safety regulations. Security: Secure control interfaces. Copy