The client wanted to optimize routes and cargo to maximize revenue and implement demand forecasting to predict upcoming cargo accurately. The airline sought to optimize the weight and chargeable weight of the cargo to facilitate efficient and cost-effective loading onto the aircraft. The air cargo industry’s intricate, constantly evolving nature made it unfeasible to achieve the above objectives through manual or rules-based methods.
The AirlineAi suite of Machine Learning-powered solutions for palette optimization incorporated inputs from a range of sources, such as airplane types, palettes, routes, demand, pricing, seasonality, airport, and the client. A dynamic programming algorithm was implemented to optimize cargo, routes, and demand in real time, considering air cargo's complex and dynamic nature. AirlineAi utilized a range of adjustable parameters to deliver the most efficient and cost-effective air cargo business outcome, while optimizing revenue and costs.
