Summary Lecture #2 Cont Approximate models to minimize sum of passenger delay From Model #1, estimate delay if itinerary is disrupted From Model #2, limit the number of itinerary copy to include only good ones Objective function: minimizing estimated passenger dissatisfaction Fine grained down to Passenger Name Record Assign a cost(expected future revenue loss of delay d for PNr p) based on: Fare class Disruption history Loyalty( FFP) Same objective can be used in sorting passengers for recovery prioritySummary Lecture #2 (Cont.)
Approximate models to minimize sum of passenger delay • From Model #1, estimate delay if itinerary is disrupted. • From Model #2, limit the number of itinerary copy to include only good ones.
Objective function: minimizing estimated passenger dissatisfaction • Fine grained down to Passenger Name Record • Assign a cost (expected future revenue loss of delay d for PNR p) based on: ƒ Fare class ƒ Disruption history ƒ Loyalty (FFP) • Same objective can be used in sorting passengers for recovery priority