Presentation on theme: "H.-S. Jacob Tsao, Wenbin Wei, Agus Pratama and Suseon Yang"— Presentation transcript:
1 Integrated Taxiing and Take-Off Scheduling for Optimization of Airport Surface Operations H.-S. Jacob Tsao, Wenbin Wei, Agus Pratama and Suseon YangCollege of Engineering, San Jose State University,San Jose, California, USAPosted at
2 Optimization of Airport Surface Operations Background and MotivationUS Air TransportationNecessity to Optimize Airport Surface OperationsA Wide Spectrum of Decision-Support ProblemsSalient Features of the Optimization ProblemAn optimization Architecture, reported separatelyOur Focus on Control: taxiway and take-off schedulingSolution Approach to Efficient, Fair and Safe Control (of Aircraft Movements)Decision Variables, Objective and ConstraintsImplementation and Numerical ResultsConclusion
3 Datalinked Clearances Control Tower Flight-Deck Automation Tower AutomationDatalinkedClearancesControlTowerFlight-DeckAutomation3/18/20083
5 Background and Motivation US Air TransportationRunways being the bottlenecks, at airport & AIRSPACENo more space for airport expansion: planning horizonNoise concerns, where there is space for new runwaysMarket driven: 10 departures at same timeCarrier gaming: false departure-time forecasts for FCFSHuman factors: controller and pilotCONGESTIONNecessity to Optimize Airport Operations, DespiteSobering from the “excess era” of the 1990’s: frequent flights, small planes; high and volatile fuel prices
6 Current Airport Surface Operations Air Traffic Controllers plan and control aircraft movements, real-time and primarily manuallyPriority:Safety is the primary concern.Fairness is secondary.Efficiency is tertiary.Result:Congestion on taxiways and runway entrances: delays and ripple/cascading effectsStop-and-go movements: wasted fuel, unnecessary emissions, noise, etc.
7 Decision Support: Problem Features Salient Features of the Operations OptimizationCrux: Runways being the primary bottlenecksAircraft sequencing:large safety air-separation required for small following largeAir-separation also dependent on direction aftet take-offAir carrier marketing and hub-and-spoke network structureStochasticity/Uncertainty:Time of readiness for departure or time of arrivalAir carrier gaming: false forecasts of readiness time for departure for First Come First Serve (FCFS) control policyPushback from gate as soon as ready for FCFS & “fairness”Resulting congestion on the taxiwaysHuman Factors: Controller and Pilot Workload
8 Decision Problems: Needs & Our Focus An overall optimization architecture, as contextInstructions for 4-D trajectories for efficient, fair and, of course, safe control (of aircraft movements),In presence ofHuman-Factors limitationsStochasticity/uncertaintyWith the assistance ofOperational proceduresMathematical optimization and algorithmsAdvanced TechnologiesControl difficulty and inefficiency as Input to longer-term planning
9 Problem Statement: Integrated Taxiway and Take-Scheduling Existing Literature:Little on optimization architecture for ASOComponent problems, treated mostly as independentTaxiway scheduling by Smeltink et al. Aircraft sequencing for take-off optimization, e.g., Anagnostakis Our Contribution, Thanks to NASA SupportArchitecture, reported separately“Derived” from salient features of ASO optimization: runways as the bottlenecks, uncertainty, human factors, fairness, etc.Operational procedures, advanced technologies and mathematical algorithms, integrated also with strategic planningIntegrated Taxiway and take-off scheduling
10 Solution Approach to Efficient Control (of Aircraft Movement) 4-D trajectories: continuous time and continuous spaceControl decisions about discrete times of aircraft reaching discrete intersections on taxiwaysTransforming an complex optimal-control problem to a mathematical programming problemDecisions embellished to build 4-D trajectoriesAnticipation of deviation from instructions due to human factors before implementation of technologies for Instruction adherenceReduce stochasticity/uncertainty for better resource utilization
11 General Strategies and Requirements Runway bottlenecks: a small queue to avoid spoilage, due to human factorsStochasticity/Uncertainty:penalty for inaccuracy of forecast departure readiness timesInclusion of only aircraft ready for near-ready for departure (i.e., pushback) from gatesmooth travel and gate-hold to avoid taxiway congestionFairnessSafety, of course, and Other Requirements
12 Input Airport Configuration A planning horizon Flight schedule One route per aircraft, departing or arrivingAir-separation required between any pair of aircraft, depending on their sizes and the directions (i.e., “departure fixes”) after take-offOptional: Locations of aircraft already on tarmac (i.e., taxiway or runway entrances)
13 Decision VariablesTime epoch of aircraft i reaching intersection u , not continuous 4-D trajectoriesImplied and implicit are sequence of take-off at a runway and sequence of reaching an intersectionAdjacency binary variable =1 if and only aircraft j follows immediately aircraft i at intersection uneeded to formulate safety-separation requirements of aircraft on the ground and in the air:Other derived variables, e.g., binary predecessor variables
14 The Objective Function To minimize the total, across all aircraft within scope, weighted sum ofWaiting time at the runway entrance: lowest weight, to encourage use of the small queue and to avoid spoilage of take-off slotsWaiting time at the gate: medium weight, to implement gate-hold when no room for waiting at the runway entranceTime spent on the taxiway: highest weight, to discourage crowding up the taxiway
16 Constraint Categories Consistency between times reaching intersections and flight adjacency for each intersectionSmooth Travel: min and max speedModeling the slots of a small queue as nodes with connecting links of 0 lengthSafety separation, on the ground and in the airFairnessOther movement-logic and operational constraints
17 ConstraintsC1: An arriving aircraft starts taxiing off the runway exit immediately after landing,C2: The time at which a departing aircraft i reaches the first node of its route is no earlier than its time of readiness for pushback.C3: To satisfy the requirement imposed by air traffic control, e.g., the National Ground Delay Program dictating a time window for departure of a flight in order to cope with congestion at another airport or in the airspace
19 Constraints (Cont’d)C4: To ensure smooth travel, we require that the speed of an aircraft be within a given range.C5: Definition of Immediate Predecessors:C6: Definition of Predecessors:C7: In terms of and , the following constraint prevents overtaking:C8: The following constraint prevents head-on collision of two aircraft in a link (u,v):C9: Aircraft must be separated for safety.
21 Constraints (Cont’d)C10: The small queue at a runway has a limited capacity, and the capacity can be modeled as a sequence of virtual links that have zero length.C11: We impose the following constraint to ensure that the release time for departing aircraft i is no sooner than when it reaches the runway entrance,C12: Departing aircraft must be safely separated in the air.
23 Constraints (Cont’d)C13: If an aircraft is released for take-off at a particular time at the runway entrance, i.e., the last artificial node (or queueing slot) of the assigned runway, its immediate follower cannot reach the runway entrance any earlier.C14: To ensure that the time at which a departing aircraft i reaches queueing slot k+1 is not earlier the time at which it reaches queueing slot k,C15: Finally, we impose the following fairness constraintC16: Binary and non-negativity constraints:
25 Implementation Dallas Fort-Worth International Airport (DFW) One quarter of DFW onlyOne departure runway and one arrival runwayDemand: 15 to 20 flights in 30 minutes1101 binary variables; 132 real-valued variables7538 integer functional constraints; 219 real onesSome key parametersWeight for wait at small queue: 0.5Weight for wait at gate: 0.75Weight for time spent on taxiway: 1Implemented with ILOG-CPLEX on a laptop
26 Numerical Results Numerical Results: Very Promising Aircraft take-off sequencing achieved: e.g., s-l-s-l-s-l re-sequenced to s-s-s-l-l-lfrom same terminal area; on same route; to same runwayin sequence of time of departure-readiness (i.e., readiness for “pushback”)as long as delays to aircraft do not exceed preset criteriaThe small runway queue always filled first and then followed by gate-holding; smooth travel on taxiwayComputation time: optimality of mixed-integer linear program reached in minutes, although the optimal integer solution is found in a fraction of time
27 Numerical Results (Cont’d) Sources of computational requirement: contentionPrimary: schedule intensitySecondary: route diversityComputation time to reach optimality of program15 flights randomly over 30-minute span: one second or less15 flights clustered over 6-minute span: 30 seconds15 flights clustered over 3-minute span: 350 secondsHowever, 99% optimality reached in 10% time.Taxiing only, e.g., set to 0.01, requiring only 3 seconds for all cases
28 ConclusionPromising decision-support for efficient, fair and safe airport surface operationsFuture work, for next two years and beyondReorderingRunway crossings, but perimeter taxiway just implemented for one quadrant of DFW and to become a new standard, for safety, etc.Deicing, but new technology for special liquid spray being tested to avoid the complexityLarger network, e.g., full DFW; higher demandFull-scale implementation, subject to NASA decision