lec13_aop3

lec13_aop3 - Airline Operations Lecture #3 1.206J April 29,...

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Airline Operations Lecture #3 1.206J April 29, 2003
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Summary Lecture #2 Achieving good passenger service reliability at an acceptable operating costs Disrupted passengers suffer long delays on average (320 minutes) versus non disrupted passengers (14 minutes) Connecting itineraries have a much higher risk of being disrupted than local itineraries (2.7x) Late disruptions are often difficult to recover the same day, much higher flight delay and cancellations at the end of the day Delays accumulate along the day, resulting in relatively high percentage of overnight passengers among disrupted (20%), but still small percentage (0.7% of passengers)
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3ODQQHG $UULYDO 7LPH KRXUV 3RVLWLYH IOLJKW DUULYDO GHOD\ PLQXWHV Average flight delay per hour, August 2000
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Our approach Wisely postpone artificially departures to maintain bank integrity and prevent passengers from missing connections Wisely canceled flights if necessary to prevent delays to propagate and the negative effects on passengers We want our solutions to be feasible for aircraft (maintenance) and crews (schedule) Guarantee solution feasibility: ¾ Artificially postponing flight departures does not disrupt more crews: Maintain flight sequence feasibility (duty) Does not include flight copies that violate crew regulation (Maximum Duty Elapsed Time) Do we guarantee maintenance routing feasibility?
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Summary 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
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pp pP t ff tT f tt (f,t) In(j) (f,t) Out(j) pf tu fg p g C(u)d(g) a(f) , a f Minimize n st : x z 1 xy xyR e s ( a , f t , ) z xx 1 [0;1]; x {0,1}; y 0 −+ ∈∈ •• ∈<  ×ρ   += + ρ ≥ +− ρ ≤ ρ ∑∑ ¾ Objective: Minimize sum of disrupted passengers ¾ Flight coverage constraints ¾ Aircraft balance for each sub fleet type ¾ Initial and end of the day aircraft resource constraints ¾ Passenger cancellation constraints ¾ Missed connected passengers constraints ¾ Only flight copy variables, x, have to be binary
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This note was uploaded on 11/08/2011 for the course AERO 16.72 taught by Professor Hansman during the Fall '06 term at MIT.

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lec13_aop3 - Airline Operations Lecture #3 1.206J April 29,...

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