Flight Rescheduling
IEOR 4405 Production Scheduling (Spring 2016)
Jo-Anne Loh (jl3963)
Anna Ming (axm2000)
Dhruv Purushottam (dp2631)
Outline
• Background
• Situation 1
– Model I
– Model II
– Comparison
• Situation 2
– Model III
• Extensions
Background
• Airlines and their delays affect the economy
– In 2007, passengers experienced a total delay of
~30,000 years with a cost of $16.1B in the US
• How should flights be effectively rescheduled?
– Minimize flight delay
– Minimize passenger delay
– Minimize revenue loss in emergency situations
Situation 1
• Currently airlines reschedule to minimize flight delay.
Does this also minimize passenger delay?
• Goal: Minimize passenger trip delay using Schedule
Minimization foR Generalized Operational Logistics
(SRMGOL) algorithm
• Assumptions:
–
–
–
–
24 hour window
Flights are on a normal distribution
Flights do not depend on previous flights for the aircraft
All passengers have 1 stop over
Situation 1: Data
1) Create initial flight times between 6 airports
2) Add average flight times to create final flight time
3) Create possible itineraries for passengers and
generate final arrival time
4) Use percentage late data of each airport to
determine which flights are delayed randomly
5) Run algorithms on passengers that miss the next leg
of their flight
Situation 1: Data Appendix
Average
Flight
Time
(BTS)
Airports
ORD
ORD
Percent
Flights
Late
JFK
LAX
MIA
ATL
IAH
Airports
0
131.75
259.06
177.91
113.76
156.89
ORD
JFK
161.16
0
365.5
187.08
150.31
240.82
LAX
235.24
318.02
0
290.87
318.02
MIA
191.15
173.91
333.73
0
ATL
122.1
139.04
288.62
IAH
152.18
212.43
212.27
Average
Delay
Time
(BTS)
Airports
ORD
ORD
ORD
JFK
LAX
MIA
ATL
IAH
0
27.72
24.27
24.11
24.83
27.84
JFK
28.66
0
17.96
18.73
19.24
30.78
318.02
LAX
19.57
18.63
0
18.21
17.33
20.83
117.14
164.74
MIA
25.56
25.6
26
0
18.62
19.41
113.36
0
130.51
ATL
22.7
23.55
20.05
16.16
0
20.35
142.87
120.76
0
IAH
24.94
18.56
25.26
17.64
21.37
0
(Random
Passenge between
rs
118-179)
JFK
0
LAX
MIA
ATL
IAH
Airports
56.83
62.05
65.68
59.78
ORD
58.38
64.69
65.79
68.58
ORD
JFK
LAX
MIA
ATL
IAH
0
160
175
121
119
131
JFK
135
0
175
154
178
120
JFK
74.26
71.53
0
LAX
55.25
59.6
0
50.6
51.94
53.54
LAX
146
121
0
175
136
119
MIA
62.96
68.74
56.27
0
42
55.31
MIA
124
174
151
0
171
177
ATL
66.62
59.57
45.92
52.91
0
59.97
ATL
172
119
170
122
0
166
IAH
61.3
56.66
52.29
55.32
63.42
0
IAH
179
143
156
129
151
0
Model I Algorithm
• The intuitive solution: minimize flight time by
putting passenger on the next available flight
• Prioritizes planes arriving at original schedule
Model II Algorithm
Reschedule passenger on
the next available flight
or hold the connecting
flight for a period of time
to allow the passenger to
make the flight.
Situation 1: Model I vs II
• ~20,000 passengers, 300 flights
• Total hours of passenger delay:
Dataset
Model I
Model II
1
70019
66819
2
79691
72491
3
80985
71621
4
80115
72680
5
80967
65727
Situation II
• Aircraft Recovery Problem – unforeseen events
disrupt a flight schedule
– Ex. In a snowstorm many flights are delayed. Which flights
should go first with a limited amount of aircrafts?
• Goal – reduce losses as much as possible
• Assumptions:
– There are less aircrafts than flights
– Flights are full
– Relax cancellation constraint
Model III
Model III Data
Model III Results
Sample run using AMPL and Gurobi
Extensions
• Situation I
– Include trade-off between passenger delay and airline costs
• Situation II
– Create a dynamic model that can constantly update the
unscheduled flights and rerun
• Combine the two models into a real world situation
where there are aircraft shortages and a need to
minimize passenger delays