Nimbus

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Inspiration

Our inspiration at Team Nimbus was to tackle the real-world challenge of a vehicle service scheduling.

What it does

Our project is a sophisticated scheduling system that manages appointments for vehicle servicing. It efficiently allocates service booths to various vehicle types while adhering to operational constraints like service hours and booth availability. The system is designed to maximize booth utilization, minimize customer turnaways, and calculate key operational statistics such as revenue, lost revenue, and unused bay time.

How we built it

We built this system using Python using it's libraries to build our solution. Our solution included parsing appointment data from CSV files, managing service booth availability, and scheduling appointments.

Challenges we ran into

The main challenges we encountered included handling complex scheduling logic, especially for accommodating different vehicle types with distinct service times. Moreover, parsing and cleaning data from CSV files to handle real-world anomalies required a keen eye for attention. Additionally, ensuring that the scheduling algorithm adheres to operational hours while maximizing booth utilization was a significant challenge.

Accomplishments that we're proud of

Our system's ability to provide detailed operational statistics, like revenue generation and bay utilization, is an achievement that we believe can greatly benefit service-oriented businesses. The robustness and adaptability of our system to handle different scenarios is another accomplishment we are proud of as well as the ability of our algorithm.

What we learned

We gained deeper insights into resource management and scheduling algorithms. We learned the importance of clean and structured data for efficient processing and decision-making. The project also enhanced our skills in Python programming.