Turnaround prediction concept: proofing and control options by - - PowerPoint PPT Presentation

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Turnaround prediction concept: proofing and control options by - - PowerPoint PPT Presentation

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Fakultt Verkehrswissenschaften Institut fr Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Turnaround prediction concept: proofing and control options by microscopic process modelling GMAN proof of concept &

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Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

ICRAT 2014

Turnaround prediction concept: proofing and control options by microscopic process modelling

GMAN proof of concept & possibilities to use microscopic process scenarios as control

  • ptions

Bernd Oreschko, Thomas Kunze, Tobias Gerbothe and Hartmut Fricke Chair of Air Transport Technology and Logistic, Technische Universität Dresden, Germany

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Turnaround Prediction and Controlling Slide 2

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Dipl.-Ing. Bernd Oreschko

Research Actitvities

  • Trajectory Management
  • Uncertainty in 4D-Trajectories
  • Safety & Security Assessment
  • Simulation Based Risk-Analysis
  • Terminal Operations
  • Expert knowledge exchange
  • Airport & Terminal Operations
  • Turnaround prediction and steering
  • Pushback and Deicing Management
  • Other

Chair of Air Transport Technology and Logistics

A320 Cockpit Simulator Safety Analysis in TMA

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Turnaround Prediction and Controlling Slide 3

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation & Background Research Review Turnaround Prediction Modell GMAN Process Controlling by Microscopic Modelling Conclusion & Outlook

1 2 3 4 5

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 4

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

EUROCONTROL Airport Collaborative Decision Making Improves situational awareness and rises efficiency by

  • Better information connection and sharing for all airport partners
  • Therefore better capacity use when information is used correctly

 Establishing Milestone concept

Impacts for the Turnaround:

  • Prediction of

 Target Off Block Time (TOBT) & Turnaround Time (TTT)

HOW? => knowing process characteristics for all process and interconnections and control options

Background - ACDM

ONBLOCK OFFBLOCK

CDM Milestones

Inbound Outbound Turnaround

20’ to 40’ TOBT Update

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 5

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation Turnaround Uncertainty

ONBLOCK OFFBLOCK

Turnaround

t

Uncertainty of start and duration cause of several factors, e.g. delays, extended sub-process duration, airport type or staff skills

Deterministic TA-Planning does not work

? ?

Best Guessing by Ramp and Ops Agents does not fit in 4D/ACDM Environment

!

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 6

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation –Modelling for DST

2 1 4 5

Überschrift 1 – Verdana 10

3 7 6 8

The Turnaround within adjacent ATM-tools inline with EUROCONTROL perspective: AMAN – Arrival Management Tool DMAN – Departure Management Tool SMAN – Surface Management Tool Turnaround Modell Output useful for SMAN and DMAN

SMAN DMAN

EXOT ONBLOCK OFFBLOCK

Turnaround

AMAN SMAN

EXIT

t

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 7

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation – new DST for Turnaround - GMAN

SMAN DMAN

EXOT

AMAN SMAN

EXIT TTT ONBLOCK OFFBLOCK

Turnaround

t

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 8

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation – new DST for Turnaround - GMAN

The GMAN output may be used in ramp operations control or schedule planning the following ways:

  • Perform Critical path analysis of TA process
  • Analyze expected buffers between processes constituting a TA event
  • Identify non-achievable target times at earliest times
  • Identify excessive process durations.

AMAN SMAN

EXIT TTT

SMAN DMAN

EXOT

t

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 9

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation & Background Research Review Turnaround Prediction Modell GMAN Process Controlling by Microscopic Modelling Conclusion & Outlook

1 2 3 4 5

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 10

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Turnaround Research findings by TU-Dresden

  • Field measurements and data analysis on several airports (MUC, FRA, STR,

HAM, DRS, LEJ) show a discrepancy between scheduled and actual times:

  • Actual Turnarounds don’t fit fixed plans
  • Process durations and buffers influenced by

 Delays  Airport (category) => Staff Skills

Dipl.-Ing. Bernd Oreschko

Delay influences on process durations & buffers

Reality

Unloading - delay

0,02 0,04 0,06 0,08 0,1 0,12 0,14 2 4 6 8 10 12 14 unloading rate [seconds/PAX]

  • ccurance

Supply Basis Hub

A319 Turnaround Plan, source: Airbus SAS

Unloading - no delay

0,02 0,04 0,06 0,08 0,1 0,12 0,14 2 4 6 8 10 12 14 unloading rate [seconds/PAX]

  • ccurance

Supply Basis Hub

Example Process Variations due to Airport Category & Delay - Unloading

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Turnaround Prediction and Controlling Slide 11

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

TUD Turnaround Researches

  • The sub-processes comprising a TA should be modeled

stochastically as they have uncertainty associated with their processes duration.

  • The TA process is dependent on various parameters like airport

category and operational factors (e.g. passenger number, airline, aircraft type), and these information can be obtained from different sources.

  • Incoming delay has an important influence on the individual sub-

process duration and process interaction times (buffers).

  • See ICRAT Contributions of last years – and others:

www.ifl.tu-dresden.de

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 12

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation & Background Research Review Turnaround Prediction Modell GMAN Process Controlling by Microscopic Modelling Conclusion & Outlook

1 2 3 4 5

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 13

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

TTT Prediction and Controlling – Two Step Approach

1. Prediction of TTT and Process Duration with stochastics

  • => comparison with other target times (e.g. TSAT)

2. Control Options by microscopic task simulation

  • => possible handling options
  • Dipl. Ing. Bernd Oreschko

GMAN

TTT Prediction

Mircoscopic Process Simulation

Control Option 1 Control Option .. Control Option n

Target Time comparision

e.g.: cTTT = TSAT ?

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Turnaround Prediction and Controlling Slide 14

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

GMAN – Process Description

  • Prediction is based on single process

description and their interaction results

  • Described Processes:
  • deboarding, catering, fuelling,

cleaning, boarding, unloading, loading (other possible)

  • Processes description:
  • Each of these process duration

and Start time is stochastically described

  • Description source:
  • empirical data from aircraft
  • perators, airports and ground

handling companies are used

Process

Duration Start

  • influence of the following parameters:
  • Aircraft type
  • Airline
  • Airport inbound and outbound
  • Airport where the TA is processed
  • Flight distance to destination
  • Flight type, i.e. low cost or legacy
  • Incoming delay (on gate)
  • Number of passengers inbound

and outbound

  • Type of aircraft stand
  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 15

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

GMAN – Critical Path Calculation

  • Dipl. Ing. Bernd Oreschko

IBT Catering

Duration Start

Fuelling

Duration Start

Cleaning

Duration Start

Deboarding

Duration Start

Boarding

Duration Start

Critical Path

TOBT

TTT

GMAN critical path calculation for one run out of n - with stochastic process start times and duration description

Repeated n-times

Unloading

Duration Start

Loading

Duration Start

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Turnaround Prediction and Controlling Slide 16

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

GMAN – Stochastic Process Description

  • Finally, probability distribution

functions can be fitted to the collected/empiric data. The Turnaround Modell is designed to allow using any distribution functions, while

  • Deterministic (fallback

level) and

  • Weibull distribution

fit best. Further more arrays of single notfitable times can be used

  • Output for GMAN: Qantiles

Possible Output for DST-Tool GMAN: Qantiles

Process / TTT

Duration Start

  • Dipl. Ing. Bernd Oreschko

Reliability  Accurateness

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Turnaround Prediction and Controlling Slide 17

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

GMAN-Look-Ahead Time

  • A higher prediction

accuracy level requires more reliable information for better stochastic process description

  • as the LAT decreases,

more accurate trigger information is expected to become available, and therefore a more specific stochastic process description

  • ut of the empirical

database can be gathered and fitted

Aircraft Characteristics Ground Operation Manuals Service Level Agreements Trigger Information Sources Seasonal Flight Plan Operational Flight Plan Fuel Order …

Data Substitution

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 18

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

GMAN Prototyp

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 19

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Proof of Concept (POC) at LEJ Airport

  • Dipl. Ing. Bernd Oreschko
  • LEJ is a medium sized airport with

mainly domestic and European flights

  • Modification of the GMAN model due

to the lack of necessary date (no Timestamps at the a/c)

  • start and end times of

deboarding, fuelling, unloading, loading and boarding were adjusted, s/aIBT and s/aOBT also available

  • =>adoptions to GMAN model
  • No catering and cleaning process

(minimum amount of occurrences

  • f these processes on the critical

path in LEJ)

  • Aim of POC:
  • Does the GMAN gives a usable

TTT prediction

Overview LEJ Airport PAX Facilities Source: airportzentrale.de

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Turnaround Prediction and Controlling Slide 20

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

POC at LEJ Airport – Empiric Data Source

  • Dipl. Ing. Bernd Oreschko

Preparation of Empiric Data (>10.000 data) from IFL Database 1. all TA with a scheduled TTT (SOBT-SIBT) above 2 hours were skipped => # 8.150 2. Data class preparation by trigger information:

  • airline => main, charter and low-cost classes
  • aircraft type by the maximum seats available =>
  • eg. ac100- (up to 100 seats), ac156 (101 up to 156 seats)
  • passenger numbers inbound / outbound by cluster of 2:
  • eg. pax25 (0 -25 passengers), pax50 (26-50 passengers)

3. creation of classes for start times and durations, regarding the trigger information

  • boarding, deboarding, loading and unloading:

1. aircraft type 2. corresponding passenger number class

  • Fuelling

1. aircraft type 2. the destination airport

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Turnaround Prediction and Controlling Slide 21

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

POC at LEJ Airport

  • Dipl. Ing. Bernd Oreschko
  • GMAN connected to the local airport information network
  • Only final trigger information => no intermediate steps => no LAT analysis
  • Data analysis for 600 turnarounds in 09/2013
  • Output of the GMAN:
  • stochastic values of TTT for a single TTT
  • Mean, μ, δ
  • Manual match of predicted TTT and ATTT by operational staff

=> Questions:

  • Does the prediction cover with the reality?
  • Indications to what should be the target values for the GMAN output?
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Turnaround Prediction and Controlling Slide 22

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

POC at LEJ Airport -Output

Deviation of ATTT to GMAN TTT prediction, all flights

  • Dipl. Ing. Bernd Oreschko
  • No clustering by trigger information
  • No good prediction

=> Clustering necessairy

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Turnaround Prediction and Controlling Slide 23

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

POC at LEJ Airport – Output

Deviation of ATTT to GMAN TTT prediction, A319 to CGN and STR

  • Dipl. Ing. Bernd Oreschko
  • Lowcost Turnaround (25 min)

=> Simple/stable TA, acceptable prediction

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Turnaround Prediction and Controlling Slide 24

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation & Background Research Review Turnaround Prediction Modell GMAN Process Controlling by Microscopic Modelling Conclusion & Outlook

1 2 3 4 5

  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 25

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Turnaround – Process Charts

  • Developing process chart to cover all elementary steps of turnaround

processes

  • Dividing process into tasks,

  ?  Overall TA  Relevant steps of aircraft catering

Dipl.-Ing. Bernd Oreschko

Turnaround Time Stamps Passenger Services Aircraft Services Baggage Mail Cargo Services Turnaround Time xOBT Deboarding Catering Cleaning Security Unloading Loading Fuelling Water Waste Water Ground Power Air Conditioning Air Starter Start: Unloading End: Air Conditioning ≠ End: Cleaning End: Catering End: Deboarding End: Unloading Pushback Pushback Deicing Start: Headcounting Boarding Baggage Start: Security Start: Pushback End: Pushback Start: Deicing End: Deicing End: Air Starter Start: Air Starter End: Headcounting End: Loadding ≠ Headcounting End: Boarding Start: Boarding Start: Loading ≠ End: Security End: Waste Water End: Water End: Fuelling Start: Air Conditioning Start: Waste Water ≠ Start: Water Start: Fuelling Start: Cleaning Start: Catering ≠ Start: Deboarding ≠ Start: Groundpower xIBT
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Turnaround Prediction and Controlling Slide 26

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Example: Microscopic Cleaning Model

  • Identification of significant cleaning steps: remove, clean, restock,

arrange for seats, lavatories, galleys, vacuum

  • Define sequence of steps using different scenarios (sequence, staff

usage) , as different control options

Dipl.-Ing. Bernd Oreschko

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Turnaround Prediction and Controlling Slide 27

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Cleaning – Progress

Progress of each cleaning step using expected value of duration Remarkable inter-process dependencies, not easy to cover with analytical description

Dipl.-Ing. Bernd Oreschko

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Turnaround Prediction and Controlling Slide 28

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

TTT Simulation with Microscopic Processes

  • Dipl. Ing. Bernd Oreschko
  • Simulation shows anticipated behavior
  • Next step is to prove the usability in live environment
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Turnaround Prediction and Controlling Slide 29

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Motivation & Background Research Review Turnaround Prediction Modell GMAN Process Controlling by Microscopic Modelling Conclusion & Outlook

1 2 3 4 5

  • Dipl. Ing. Bernd Oreschko
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SLIDE 30

Turnaround Prediction and Controlling Slide 30

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Conclusion and Outlook

  • Proof of concept of TTT Prediction is confirmed
  • The different levels do not generally imply that the TA process time

prediction will get smaller by default (or even show a smaller variation)

  • But the reliability of the results increases
  • GMAN principle proofed:
  • Identify non-achievable target times (at earliest times)
  • Identify excessive process durations
  • Output with quality information
  • Next Step:
  • Test and Validation of control options (in LEJ)
  • Dipl. Ing. Bernd Oreschko
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Turnaround Prediction and Controlling Slide 31

Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs

Bernd Oreschko Chair of Air Transport Technology and Logistic Technische Universität Dresden, Germany

  • reschko@ifl.tu-dresden.de
  • Dipl. Ing. Bernd Oreschko