Abstract: Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.

Abstract: Considering the dependency of a flight hold time on multitude of dynamically varying factors, determining an optimal hold time balancing between passenger utility and airline utility is challenging. State of art approaches are limited to use of only deterministic approaches with limited ML assistance that require huge labelled training data. Embodiments disclosed herein provide a method and system for computing and recommending optimal hold time for every flight of an airline so as to minimize passenger misconnects in airline operations through Reinforcement Learning (RL). The method disclosed utilizes RL, which is trained to make decision at a flight level considering local factors while still adhering to the global objective based on global factors. Further method introduces business constants in the rewards to the RL agents bringing in airline specific flexibility in reward function.