Presentation on theme: "H.-S. Jacob Tsao, Wenbin Wei, Agus Pratama and Suseon Yang"— Presentation transcript:
1Integrated 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
2Optimization 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
3Datalinked Clearances Control Tower Flight-Deck Automation Tower AutomationDatalinkedClearancesControlTowerFlight-DeckAutomation3/18/20083
5Background 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
6Current 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.
7Decision 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
8Decision 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
9Problem 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
10Solution 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
11General 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
12Input 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)
13Decision 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
14The 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
16Constraint 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
17ConstraintsC1: 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
19Constraints (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.
21Constraints (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.
23Constraints (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:
25Implementation 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
26Numerical 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
27Numerical 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
28ConclusionPromising 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