This document summarizes a presentation on analyzing airport terminal effectiveness using multiple criteria and simulation optimization. It discusses modeling the terminal facilities planning problem (TFPP) as a discrete event simulation and multi-objective optimization problem. Specifically, it formulates the TFPP as a bi-criteria problem (2TFPP) to optimize configuration cost and average waiting time. It then describes solving the 2TFPP using a pre-computing phase to derive Pareto optimal solutions, followed by a decision-making phase where the decision maker selects the preferred solution based on their preferences.

1. Multiple Criteria Analysis of the Airport
Terminal Effectiveness by Multi-objective
Optimization and Simulation
ICMSDM ′2016
Janusz Miroforidis, Ph.D.
Systems Research Institute,
Polish Academy of Sciences,
Warsaw, Poland

2. 2
Presentation plan

Terminal Facilities Planning Problem (TFPP)

Discrete-event simulation model for TFPP

Multi-objective methodology

Bi-criteria formulation of TFPP (2TFPP)

Solving 2TFPP

Conclusions

3. 3
Terminal Facilities
Planning Problem (TFPP)
Departure Terminal — a complex system
• Passengers ‒
terminal facilities
interaction (check-in
desks, security
control desks, stairs,
etc.)
• Passenger behaviour
• Passenger flow
Source: http://www.businesstraveller.com/files/News-images/Gatwick-airport/

4. 4
TFPP (cont.)
The most general formulation
Find the best configuration of an airport terminal facilities, taking
into account: passenger arrival pattern connected to the flight
schedule; passenger moving pattern inside the terminal;
passenger service level
• How to describe configurations and the terminal operation?
• How to evaluate a configuration in a real-life scenario?
• What does „the best configuration” really mean?
• Is it worth to consider a multiple criteria formulation of TFPP?
(Yes, it is!)

5. 5
Discrete-event simulation model for TFPP
Departure terminal — a network of service nodes with
waiting queues
— a configuration, i.e. (4, 2, 2)

6. 6
The network of service nodes with waiting queues
(may be a complex graph)
Input:
Discrete-event simulation model for TFPP
(cont.)
Output:
•Avg. queue waiting time
•Avg. queue length
•Prob. of an event
•Other indicators
Model:
Output — in general, hard to give it by analytical formulas!

7. 7
The discrete-event simulation model of
a departure terminal
Input:
Discrete-event simulation model for TFPP
(cont.)
Output:
•Avg. queue waiting time
•Avg. queue length
•Prob. of an event
•Other indicators
JaamSim
Simulation Engine
+ Model:
Output — relatively easy to obtain by simulation runs!

8. 8
Multi-objective methodology
where:
vmax denotes the operator of deriving all
efficient variants (Pareto optimal) in X0
.
Multi-objective optimization problem

9. 9
Multi-objective methodology (cont.)
f2(x)
f(X0)
f1(x)
Pareto frontier
(efficient outcomes)
″the more, the better″
Solution to multi-objective optimization problem

10. 10
Multi-objective methodology (cont.)
f2(x)
f1(x)
Selection of the most preffered variant according to the
Decision Maker (DM) preferences.
?
?
?
BINGO!
Multiple criteria decision making
DM

11. 11
Multi-objective methodology (cont.)
f2(x)
f1(x)
By solving optimization problem
Deriving an efficient decision variant
where:
Scalarisation by
augmented Tchebychef metric

12. 12
Multi-objective methodology (cont.)
f2(x)
f1(x)
Expressing the DM’s preferences
By:
Simple but powerful method

13. 13
Bi-criteria formulation of TFPP (2TFPP)
Bi-criteria optimization model

14. 14
Solving 2TFPP
Pre-computing phase

15. 15
Solving 2TFPP (cont.)
Decision-making (hyphotetical) phase — one step
„the more, the better” conversion

16. 16
Solving 2TFPP (cont.)
Decision-making phase — all steps
The solution to 2TFPP:
configuration (5, 3, 2) and its
outcome (configuration cost:
15 units, avg. waiting time:
13.086 minutes).
Hyphotetical decisio-
making phase!

17. 17
Conclusions

Accurate discrete-event simulation model of
a departure terminal is requested (it can be costly!)

All objective functions should precisely reflect reality

More than two criteria?

Continuous decision variables? (the presented method can
be used after a discretization of such variables)

Deriving of efficient configurations during the decision-
making phase may be a better solution (no pre-computing
phase)
Solving multiple criteria TFPP in a real-life scenario
using presented decision-making framework

18. 18
THANK YOU!
janusz.miroforidis@ibspan.waw.pl