Collaborative Approaches to the
Application of Enroute Traffic Flow
Management Optimization Models
CHARLES N. GLOVER
MICHAEL O. BALL
DAVID J. LOVELL
Weather in the NAS
2
 Weather is unpredictable
 How long will it last?
 Where is it headed?
 How dangerous is it?
 These are research questions for meteorologists.
 These questions also affect the NAS.
The Impact on Flights
3
 Suppose many flights
are scheduled to travel
through a region of
airspace.
 A weather disturbance
in this region can
greatly reduce capacity.
 This leads to a problem of congestion in the NAS.
The Implementation of AFPs
4
Primary Route
Bad Weather in Airspace
Secondary
Route
 Airspace Flow Programs (AFP’s) are generally
implemented over volumes of airspace where a capacity
reduction is predicted over an extended period of time.
 Flights are given the choice of flying around the flow
constrained area (FCA) (with or without ground delay),
or flying through the FCA with a given amount of ground
delay.
Weather Uncertainty
5
Primary Route
Bad Weather in Airspace
Hybrid
Secondary
Route
 Given the uncertainty of weather, it is entirely
possible that the FCA clears earlier than anticipated.
 In such a case,


flights still grounded have the possibility of reducing their
delays by departing sooner.
flights that have rerouted around the FCA can possibly have
their route length shortened by now flying through the FCA.
Approaches to this Problem
6
 The problem of how best to allocate flights to FCA arrival
slots in under a reduced capacity situation was studied by
Ganji and later by Glover.
 Two different IPs were proposed which seek to minimize
the total expected delay of all flights scheduled to fly
through the FCA.
 Both IPs assume a probability distribution of possible
weather scenarios and give outputs which include
courses of action based on the weather outcomes.
 Unfortunately, neither of these models take CDM into
account.
Collaborative Decision Making
7
 Collaborative Decision Making (CDM) originates
from an understanding that neither side (the
FAA/EUROCONTROL, nor the airlines) has all the
information necessary to solve air traffic flow
problems.
 Most, if not any new major ATFM system proposed
either in the US or Europe must adhere to
Collaborative Air Traffic management principles.
GG Model and CDM
8
 In seeking to minimize the total expected delay, the
GG model assumes that the air navigation service
provider (ANSP) can exercise universal control,
which is a rare occurrence.
 So although the model they propose is powerful from
a mathematical point of view, they are unlikely to be
accepted as currently proposed.
Accounting for CDM
9
 Here, we analyze two alternative approaches for
incorporating these models into a CDM-like setting.
 In both cases, the ANSP allocates certain resources
to the flight operators, and the flight operators then
optimize the use of the resources they are given.
 This gives the flight operators the final decision on
which flights are rerouted and to adjust the ground
delay amounts.
Overview of IP
10
 Here we will describe the GG model.
 This is a two-stage stochastic IP.
 Stage one represents the initial assignment of delay
and reroutes under the assumption that the weather
lasts the entire duration.


One constraint set says that
each flight must be given an initial assignment
The other says that
the FCA capacity must be respected.
Overview of IP
11
 Here we will describe the GG model.
 This is a two-stage stochastic IP.
 Stage two represents a set of weather clearance times
(called scenarios) addressing the possibility that the
weather clears early.



In each scenario,
each flight must be reassigned
In each scenario,
the FCA capacity must be respected.
In each scenario,
we cannot violate the laws of geometry or physics.
Modification to the GG Model
12
 The adjustments we made to the GG model were
based on bringing in line with CDM principles.



Each flight now has a flight operator.
Instead of assigning FCA arrival times to flights, these
assignments are made to flight operators, who then have the
ability to distribute the delay as needed.
The basic fairness mechanism is still Ration-by-Schedule,
which is accepted.
The First Mechanism
13
 The ANSP executes RBS and allocates to each flight
operator a bundle of FCA arrival times.
 Each flight operator then makes decisions regarding
which flights will stay on their primary routes and
experience ground delay and which will be rerouted
around the FCA.
 This is functionally equivalent to the CDM
cancellation and substitution process.
The Second Mechanism
14
 The ANSP executes RBS and allocates to each flight
operator a bundle of FCA arrival times.
 Each flight operator then makes decisions regarding
which flights will stay on their primary routes and
experience ground delay and which will be rerouted
around the FCA.
 The ANSP then executes an inter-airline exchange.
 This is functionally equivalent to the CDM
compression algorithm.
Flight Operators Using the GG Model
15
 The flight operator decision step can take advantage
of the GG model.
 However, because the flight operator only knows
about their flights, this gives a limited viewpoint of
the utilization of the NAS.
 This implies that the flight operators are using an
approximate objective function, where they are
estimating future ANSP behavior.
Experiment
16
 We tested the two mechanisms versus the system optimal (provided by
the GG model).
 The input to the model consisted of 400 flights and an FCA whose
duration was expected to last 5 hours.
 We also assumed seven airlines and four aircraft types:
ERJ-170
737-300
757-200
767-400
Carrier 1
64
64
16
16
Carrier 2
30
12
6
12
Carrier 3
0
70
0
0
Carrier 4
20
8
0
12
Carrier 5
0
10
10
0
Carrier 6
20
0
0
0
Carrier 7
12
9
9
0
Airline Costs
17
 Flight operator decisions are based on minimizing





expected airline cost of delay.
The aircraft operation cost per minute of airborne delay
is $64 and $32 for ground delay.
We used a cost per minute of delay per passenger of
$3.00, of which airlines consume 1/6th of this cost.
Thus in our model for the cost of ground delay for an
airline is 32 + 0.5Psn(f)
The cost for airborne delay for an airline is
64 + 0.5Psn(f)
Psn(f) is the number of passengers on flight f.
Thank You!
18
Questions?
ANSP Costs
19
 To determine the efficiency of the solutions
produced, we executed the GG model on this input,
ignoring the carriers.
 This gives a system optimal.
 We assumed that the ANSP’s objective is to reduce
the overall expected cost of delay, using the same
figures from the previous slide.
Modeling Compression
20
 Compression can also be modeled by an objective
function (after the airlines have individually run the
algorithm themselves) via a superlinear cost
function:
 Let md = Maxf {time (goal(f)) – earliest(f)}.
 Then the cost of assigning flight f to the FCA arrival
time i is
cst(f, i) = (time(i) – (time(goal(f)) + md))1+e
 Where goal(f) is the time that the f’s carrier wishes to
assign to flight f and earliest f is the earliest time that
f can arrive at the FCA.
Total Expected Costs of Delay
21
1140000
Mechanism Performance Comparison
1120000
Total Expected Cost of Delay
1100000
1080000
1060000
SysOpt
1040000
Mech1
Mech2
1020000
1000000
980000
960000
1
2
3
4
5
Executions
6
7
8
Results & Conclusions
22
 As expected, neither MECH1 nor MECH2 received the optimal




cost, but both provided within 10% of the system optimal.
MECH2 does achieve a small, but noticeable advantage over
MECH1.
Although MECH1 provides airlines with more power in
controlling their flights, it also requires more information,
particularly when these flights will be at the FCA boundary
which can be difficult to compute.
MECH2 only requires a priority listing of the flights.
Both mechanisms reduce the role of the ANSP to one of
allocating a set of FCA arrival times to airlines and allowing
the flight operators to optimize the resources they are given.