OptiLine

Group Size

Inspiration

The inspiration for OptiLine came from the frustration of long meal wait times during events like hackathons. Seeing everyone miss out on valuable networking and project time, we set out to create a solution that enhances dining efficiency and reduces queue frustration.

What it does

OptiLine optimizes meal service by dividing attendees into dynamically assigned wristband groups. Using data-driven strategies, it effectively staggers meal access to minimize wait times and crowd congestion, ensuring a streamlined dining experience for all.

How we built it

We built OptiLine using Pyomo, a Python library for optimization. The model takes into account factors like service rates and student numbers to determine the optimal grouping and timing for meal service. We iterated on various group sizes to maintain linear constraints while achieving the best possible queue reduction.

Challenges we ran into

One of the main challenges was ensuring our model remained linear to fit within the capabilities of available solvers. We also faced the complexity of balancing group sizes against service constraints to achieve the desired wait time reduction.

Accomplishments that we're proud of

We are proud to have developed a functional model that reduces wait times through smart optimization. Our solution not only addresses a common event issue but also showcases the real-world application of operations research techniques.

What we learned

We learned the importance of iterative problem-solving and the intricacies of optimization modeling. The challenge highlighted how powerful data-driven optimization can be in creating practical, efficient solutions to everyday problems.

What's next for OptiLine

Looking ahead, we plan to enhance OptiLine by incorporating real-time data to adjust the model dynamically during events. We also aim to expand its application to other queue-heavy scenarios like conferences and festivals for a broader impact.