Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Turnaround prediction concept: proofing and control options by microscopic process modelling GMAN proof of concept & possibilities to use microscopic process scenarios as control options Bernd Oreschko , Thomas Kunze, Tobias Gerbothe and Hartmut Fricke Chair of Air Transport Technology and Logistic, Technische Universität Dresden, Germany ICRAT 2014
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Chair of Air Transport Technology and Logistics Research Actitvities Trajectory Management • Uncertainty in 4D-Trajectories • Safety & Security Assessment • Simulation Based Risk-Analysis • Terminal Operations • A320 Cockpit Simulator Expert knowledge exchange • Airport & Terminal Operations • Turnaround prediction and steering • Pushback and Deicing Management • Other • Safety Analysis in TMA Dipl.-Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 2
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation & Background 1 Research Review 2 Turnaround Prediction Modell GMAN 3 Process Controlling by Microscopic Modelling 4 Conclusion & Outlook 5 Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 3
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Background - ACDM 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 CDM Milestones Inbound ONBLOCK OFFBLOCK Turnaround Outbound 20’ to 40’ TOBT Update 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 Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 4
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation Turnaround Uncertainty Uncertainty of start and duration cause of several factors, e.g. delays, extended sub-process duration, airport type or staff skills ? ? ONBLOCK OFFBLOCK Turnaround t Deterministic Best Guessing by ! TA-Planning Ramp and Ops does not work Agents does not fit in 4D/ACDM Environment Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 5
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation – Modelling for DST AMAN SMAN DMAN ONBLOCK OFFBLOCK Turnaround SMAN EXIT EXOT t 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 Überschrift 1 – Verdana 10 1 2 3 4 5 6 7 8 Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 6
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation – new DST for Turnaround - GMAN AMAN SMAN DMAN ONBLOCK OFFBLOCK Turnaround SMAN EXIT TTT EXOT t Dipl. Ing. Bernd Oreschko 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 AMAN SMAN DMAN SMAN TTT EXIT EXOT The GMAN output may be used in ramp operations control or schedule t 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 . • Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 8
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation & Background 1 Research Review 2 Turnaround Prediction Modell GMAN 3 Process Controlling by Microscopic Modelling 4 Conclusion & Outlook 5 Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 9
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 Unloading - no delay 0,14 0,12 0,1 occurance 0,08 Supply Basis A319 Hub 0,06 Unloading - delay Turnaround 0,04 0,14 Plan, source: 0,02 0,12 Airbus SAS 0 0,1 0 2 4 6 8 10 12 14 Reality occurance unloading rate [seconds/PAX] 0,08 Supply Basis Hub Example Process 0,06 0,04 Variations due to Airport 0,02 Category & Delay - 0 Unloading 0 2 4 6 8 10 12 14 Delay influences on process durations & buffers unloading rate [seconds/PAX] Dipl.-Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 10
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 Turnaround Prediction and Controlling Slide 11
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs Motivation & Background 1 Research Review 2 Turnaround Prediction Modell GMAN 3 Process Controlling by Microscopic Modelling 4 Conclusion & Outlook 5 Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 12
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 • Control Option 1 Mircoscopic GMAN Process Control Option .. TTT Prediction Simulation Control Option n Target Time comparision e.g.: cTTT = TSAT ? Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 13
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs GMAN – Process Description Duration Prediction is based on single process • description and their interaction results Process Described Processes: Start • - influence of the following parameters: deboarding, catering, fuelling, • • Aircraft type cleaning, boarding, unloading, • Airline loading (other possible) • Airport inbound and outbound Processes description: • • Airport where the TA is processed Each of these process duration • • Flight distance to destination and Start time is stochastically • Flight type, i.e. low cost or legacy described • Incoming delay (on gate) Description source: • • Number of passengers inbound empirical data from aircraft • and outbound operators, airports and ground • Type of aircraft stand handling companies are used Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 14
Fakultät Verkehrswissenschaften Institut für Luftfahrt und Logistik, Professur Technologie und Logistik des Luftverkehrs GMAN – Critical Path Calculation Duration Repeated n-times Deboarding Duration Start Fuelling Start Critical Path Duration Catering Start Duration Cleaning Duration Boarding Start Start Duration Duration Unloading Loading Start Start TTT IBT TOBT GMAN critical path calculation for one run out of n - with stochastic process start times and duration description Dipl. Ing. Bernd Oreschko Turnaround Prediction and Controlling Slide 15
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