A desktop application developed in Python with PyQt6 to manage and simulate lift control under various configurations.
I optimised a custom algorithm for efficiency in distance travelled by the lift. This algorithm achieves up to 82.5% reduction in distance travelled compared to the naive algorithm benchmark, outperforming the 25% reduction claimed by the destination dispatch technique.
For more information about this, see the design document at docs/Lift Control - Design Document.pdf. It contains an explanation of the algorithm and performance analysis.
Compatible with:
- Windows
- MacOS
- Linux
Python 3.7.0 or later is required for this software to run. It can be downloaded from Python's website here.
To run the application, you should follow the following steps:
- Clone this GitHub repository.
- Ensure that you're in the root directory:
lift-control-simulation - Install the required Python libraries:
pip install -r requirements.txt - Run the application with the command:
python -m src.app
The user is able to configure any number of floors, number of people, lift capacity, and the delay of intervals in the user interface.
Their simulation can be visually displayed for up to five floors. For more than five floors, the application provides a non-visual display. In both cases, the user can run their simulation with either the naive mechanical lift (the benchmark) algorithm, or my improved lift algorithm.
The configured simulation remains the same until the user generates a new simulation, or changes the configuration settings. This enables the user to directly compare how the improved algorithm compares to the naive algorithm.
The application prints a temporary log of the simulations in the terminal. It records the simulation configuration, people generated, movements of the lift, people pending (if applicable), people in the lift, people delivered, and an overview of people after the simulation is complete.