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

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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

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Terminal FacilitiesPlanning 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/

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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!)

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Discrete-event simulation model for TFPPDeparture terminal — a network of service nodes with

waiting queues

— a configuration, i.e. (4, 2, 2)

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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!

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The discrete-event simulation model ofa departure terminal

Input:

Discrete-event simulation model for TFPP (cont.)

Output:•Avg. queue waiting time•Avg. queue length•Prob. of an event•Other indicators

JaamSimSimulation Engine

+ Model:

Output — relatively easy to obtain by simulation runs!

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Multi-objective methodology

where:

vmax denotes the operator of deriving allefficient variants (Pareto optimal) in X0 .

Multi-objective optimization problem

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Multi-objective methodology (cont.)

f2(x)

f(X0)

f1(x)

Pareto frontier(efficient outcomes)

″the more, the better″

Solution to multi-objective optimization problem

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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

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Multi-objective methodology (cont.)

f2(x)

f1(x)

By solving optimization problem

Deriving an efficient decision variant

where:

Scalarisation byaugmented Tchebychef metric

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Multi-objective methodology (cont.)

f2(x)

f1(x)

Expressing the DM’s preferences

By:

Simple but powerful method

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

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Solving 2TFPP Pre-computing phase

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Solving 2TFPP (cont.)

Decision-making (hyphotetical) phase — one step

„the more, the better” conversion

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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!

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Conclusions

Accurate discrete-event simulation model ofa 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

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THANK YOU!

janusz.miroforidis@ibspan.waw.pl