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Data Science and Data-driven Optimisation in Airline and Airport
Operations (AAE4009)
Lecture 01 - Introduction to applied mathematics, mathematical
optimisation and data mining
Dr Kam K.H. Ng
B.Eng. (hons) in Aviation Engineering (JS3507)
B.Eng. (hons) in Air Transport Engineering (48401)
Department of Aeronautical and Aviation Engineering,
The Hong Kong Polytechnic University, HKSAR.
Agenda
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Laboratory 2
1. Single-runway aircraft landing problem (60 marks)
2. Rationale of the selection of objective functions (16 marks)
3. Multiple-runways aircraft landing problem (24 marks)
5
Question setting
Assuming that you are the operations research team of airport system engineering company,
you are going to design optimisation models and algorithms in solving an aircraft
sequencing and scheduling problem. A sample dataset is provided by civil aviation
authority for proof-of-concept purposes, and you are going to present the results to the
authority and try to get this consultancy project.
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7
Data1.csv
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Assumptions
Deterministic.
As we have close connection with other airports and the flight information are shared, you have received the latest
schedule from the departing airports, and the scheduled gate arrival time of all flights is almost deterministic and
confirmed.
No uncertain factors.
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10
Branch and bound
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13
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Single runway
scheduling
Define set, parameters and decision variables
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Research motivation
Aircraft sequencing and scheduling problem (ASSP) has been identified as the main bottleneck and does affect
related airport facilities and operation.
ASSP includes aircraft landing problem (ALP) and aircraft take-off problem (ATP)
Special requirement in aircraft landing problem:
Separation time between two consecutive flights
Aircraft generates vortex that affect the flight safety of the following adjacent aircrafts and passenger
comfortable level.
17
Research motivation
Wastage in first-come first-serve scheduling principle
Separation time is determined by the flight classes
Trade-off between capacity and resilience
Increase the resilience of a landing scheduling & Allow breathing room
Reduce the risk of disruption during the operation, but lead to a level near to or at its
capacity
Lose certain runway resources
18
Concept of tardiness of flight
The landing scheduling and sequencing is computed by meta-heuristic, while the deviation between assigned
landing time and target landing time is formulated as a penalty function.
Less chance of disruption for the next departure of the corresponding flight and less effort spent on disruption
management for airline schedule (airline aspect).
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Single runway scheduling
Decision: who go first, who go later. => sequencing decision
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20
Concept of separation time and landing time assignment
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1
21
Write the problem formulation first.
22
Write the problem formulation first.
23
Schematic diagram of single runway scheduling
24
Multiple-runway
aircraft landing
problem
Schematic diagram of multiple runway scheduling
26
Aircraft sequencing and scheduling problem
Multiple runways scheduling
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Decision:
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1. Assign runway to aircraft => assignment
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2. who go first, who go later. => sequencing decision (onxlrstiEI.fr
the same runway)
Decision:
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1.
Assign runway to aircraft => assignment decision
2.
Check whether any pairs of two flights on the same
3.
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Aircraft sequencing and scheduling problem
Multiple runways scheduling
Decision (method 1):
1. Assign runway to aircraft => assignment problem =>
2. who go first, who go later. => sequencing decision (on the same runway) =>
Decision (method 2):
1.
Assign runway to aircraft => assignment decision =>
2.
Check whether any pairs of two flights on the same
=>
(on the same runway)
3.
if
is true,
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Method 1
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Method 2
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Alternatives?
There may another methods, but these two are the most common used one.
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Ans
Ans
Q1a.
Global optimal value 627.00
Q1b.
Global optimal value 21.62
Q3a.
Global optimal value: 36.00
Same values in both methods 1 and 2
Q3b.
Q1c.
Global optimal value: 821.00
Draw a schematic diagram of the results and
show their differences. (hand-drawing is fine)
Q1d.
Local optimal value: 2436.00 (opt gap
~61.12%) (depending on the
computational power.)
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Thank you