Airline Operations Lecture #2 1206J Apri27,2003
Airline Operations Lecture #2 1.206J April 27, 2003
Summary Lecture #1 Airline schedules(Aircraft ,crew passengers) are optimized leading to Little slacks(idle time) Schedule dependencies Delay chain effects Causes of schedule disruptions Shortages of airline resources Shortages of airport resources Complex airline resource regulations Aircraft maintenance Pilots
Summary Lecture #1 • Airline schedules (Aircraft, crew, passengers) are optimized leading to: ¾ Little slacks (idle time) ¾ Schedule dependencies ¾ Delay chain effects • Causes of schedule disruptions ¾ Shortages of airline resources ¾ Shortages of airport resources • Complex airline resource regulations ¾ Aircraft maintenance ¾ Pilots
Airline Schedules Recovery Schedule recovery Model (sRM) SRM Aircraft Recovery Model(ARM Crew Recovery Model CRM) Passenger Flow Model(PFM ARM CRM PFM Journey Management Passenger Re-accommodation Passenger Reaccommodation
Airline Schedules Recovery ¾ Schedule Recovery Model (SRM) ¾ Aircraft Recovery Model (ARM) ¾ Crew Recovery Model (CRM) ¾ Passenger Flow Model (PFM) ¾ Journey Management ¾ Passenger Re-accommodation 650 $50 &50 3)0 3DVVHQ J HU 5HDFFRPPRGDWLRQ
Summary Lecture #1(Cont) Airline schedules recovery problems r Aircraft maintenance module Objective: feasibility only Crew schedule recovery module Objective: to minimize disruptions, recover the disrupted with minimum flight schedule disruptions and control Flight Time Count Complex rules Passenger schedule recovery module Objective: to minimize passenger delays, ill will, gap between expected and delivered service Complexity: Priority rules(booked over disrupted, priority among disrupted: network, user, FFP, fare class) Seat availability uncertainty
Summary Lecture #1 (Cont.) • Airline schedules recovery problems ¾ Aircraft maintenance module: • Objective: feasibility only ¾ Crew schedule recovery module • Objective: to minimize disruptions, recover the disrupted with minimum flight schedule disruptions and control Flight Time Count • Complex rules ¾ Passenger schedule recovery module • Objective: to minimize passenger delays, ill will, gap between expected and delivered service • Complexity: – Priority rules (booked over disrupted, priority among disrupted: network, user, FFP, fare class) – Seat availability uncertainty
Lecture#2 Outline Passengers are important to satisfy Tricks to prevent schedule disruptions and recover schedules Traditional ARM: Model shortcomings Interdependency of passengers and aircraft operations Our approach: Minimizing sum of disrupted passenger Flight copy generation and solution feasibility Minimizing sum of passenger delays Proxy of minimizing sum of passenger delays Simulation environment Conclusion
Lecture #2 Outline • Passengers are important to satisfy • Tricks to prevent schedule disruptions and recover schedules • Traditional ARM; Model shortcomings • Interdependency of passengers and aircraft operations • Our approach: Minimizing sum of disrupted passenger • Flight copy generation and solution feasibility • Minimizing sum of passenger delays • Proxy of minimizing sum of passenger delays • Simulation environment • Conclusion
Importance of delivering services as expected in airline industry Very competitive industry Low profit margin(5% in 2000, best year) Dissatisfied customers might shop next to competitors, jeopardizing your profitability On time service is not prime factor to attract customers but it contributes to loyalty Passenger delay distribution is not continuous, few passengers suffer high delays Passenger dissatisfaction function with respect to delay s is not linear Clear objective minimize passenger ill will with same operations costs
Importance of delivering services as expected in airline industry • Very competitive industry • Low profit margin (5% in 2000, best year) • Dissatisfied customers might shop next to competitors, jeopardizing your profitability • On time service is not prime factor to attract customers but it contributes to loyalty • Passenger delay distribution is not continuous, few passengers suffer high delays • Passenger dissatisfaction function with respect to delays is not linear • Clear objective: minimize passenger ill will with same operations costs
Trade off. Passenger service reliability versus operating costs Admissible operating cost region Feasible operating space Passenger dissatisfaction Operating costs
Trade off: Passenger service reliability versus operating costs Operating costs Passenger dissatisfaction Admissible operating cost region Feasible operating space
Passenger bookings for each scheduled itinerary PREPROCESS Build the list of disrupted Assign all non-disrupted pass engers passengers, L to their planned itinerar ies Remove seats from r emaining inventory Sort l according to serv ice poli Record pass enger del PDC END ake next disrupted pass enger in L Passenger Delay statisti Find best recov ery itinerary and assign passenger Remove s eat from remaining inventory Record passenger del ay
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Flight and passenger delays 30 25 20 口 Passengers ■ Flight Delay Passenger/flight =170% Flight delays underestimate passenger delays Key explanation lies in the disrupted passengers
Flight and passenger delays 0 5 10 15 20 25 30 (minutes)Passengers Flight Delay Flight delays underestimate passenger delays Key explanation lies in the disrupted passengers Passenger/flight = 170%
Disrupted passengers versus non disrupted passengers August 2000 AV Delay Passengers% Delays (minutes) Disrupted 320 minutes 3.2% 40% passengers Non disrupted 16 minutes 968% 60% passengers >Disrupted passengers experience long delays in general because 20% of them are stranded overnight (delay propagation results in more disruptions later during the day) Although a small percentage, disrupted passengers account for 40% of the total passenger delay and most of the severely delayed passengers(80% of passengers delayed by more than 4 hours
Disrupted passengers versus non disrupted passengers ¾ Disrupted passengers experience long delays in general because 20% of them are stranded overnight (delay propagation results in more disruptions later during the day) ¾ Although a small percentage, disrupted passengers account for 40% of the total passenger delay and most of the severely delayed passengers (80% of passengers delayed by more than 4 hours) Non disrupted passengers Disrupted passengers August 2000 16 minutes 96.8% 60% 320 minutes 3.2% 40% % Passengers % Delays Av. Delay (minutes)
