Summary Lecture #2 Cont Minimize Sum of Disrupted Passengers(M1) Works well(20CPU) for day with severe flight schedule disruptions. Why? Because number of variables relatively small (O(F+ I)and number of constraints O(F+ D) And binary variables Downside: do not consider disrupted passenger and non disrupted passenger delays: May decide to postpone a flight by 30 minutes with 100 passenger on board to recover only 1 disrupted passenger who could have been recovered effectively Minimizing Sum of Passenger Delays M2) Problem becomes much bigger if all the recovery itineraries are included Hard to solve using B&B (M1/M2 )equivalent to FAM/ODF AM): capacity constraints tend to lead to fraction solutions of lp relaxationSummary Lecture #2 (Cont.) • Minimize Sum of Disrupted Passengers (M1) ¾ Works well (20CPU) for day with severe flight schedule disruptions. Why? • Because number of variables relatively small (O(F + I) and number of constraints O(F + I)) • And binary variables ¾ Downside: do not consider disrupted passenger and non disrupted passenger delays: May decide to postpone a flight by 30 minutes with 100 passenger on board to recover only 1 disrupted passenger who could have been recovered effectively • Minimizing Sum of Passenger Delays (M2) ¾ Problem becomes much bigger if all the recovery itineraries are included ¾ Hard to solve using B&B ¾ (M1/M2) equivalent to (FAM/ODFAM): capacity constraints tend to lead to fraction solutions of LP relaxation