Aviation: Demand Signal detection and fleet scheduling using AI
Machine learning is being applied in a variety of applications and at scale. One of the problems we tackled was private jet aviation.
The problem statement is slightly different from a regular taxi renting service, although certain principles do apply.
The primary objective is to crack the dynamic demand, predict ahead of the future and also manage the fleet movement by balancing the density across the zones.
It has had interesting complex scenarios like decoding the feasibility of travel zones and predicting the impact of new bookings on fleet schedule. Adjusting operations with dynamic demand is an on-going process and our attempt is only to keep refining and re-defining our solution.
Outline/Structure of the Case Study
- Understanding on-demand private jet
- Problem statement: a. demand signal detection b. Fleet imbalance scheduling
- Managing the data
- ML models used in solving demand detection
- Optimization using matching algorithm
- Road ahead
- Final words
- Defining the dynamic demand, fleet imbalance and scheduling problem in private jet.
- Exploring the approaches used to solve these problems.
- This is a design discussion and the expectation is know the complexity involved in real problems and how to go about solving these with ML techniques.
DATA SCIENCE ENTHUSIASTS
Prerequisites for Attendees
- Basic concepts of Machine learning.
- Demand forecasting problem in Machine learning.
- PCA, Time series, Ensemble know-how.