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Optimal Metering Policies for Optimized Profile Descent Operations at Airports Heng Chen and Senay Solak Isenberg School of Management University of Massachusetts Amherst ICRAT 2014, Istanbul May 30, 2014 Outline Motivation Problem

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Optimal Metering Policies for Optimized Profile Descent Operations at Airports

ICRAT 2014, Istanbul May 30, 2014

Heng Chen and Senay Solak

Isenberg School of Management University of Massachusetts Amherst

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2 Optimal Metering Policies for OPD

Outline

  • Motivation
  • Problem Framework and Algorithmic Design
  • Stochastic Programming Model
  • Convexification and Lagrangian Decomposition
  • Numerical Implementation and Conclusion
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3 Optimal Metering Policies for OPD

Motivation: Optimized Profile Descent

  • Capacity limits in airport; significance of fuel and

environmental costs

  • Optimized Profile Descent (OPD) helps improve efficiency in

these areas

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4 Optimal Metering Policies for OPD

Motivation: Optimized Profile Descent

  • High trajectory flight results in decreased noise levels
  • Reduced thrust (near idle thrust) during descent

results in less fuel burn cost and emissions

  • Flight tests suggest 30% reduction in noise and

emissions; 25-50 gallons reduction in fuel consumption

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5 Optimal Metering Policies for OPD

Motivation: Current Practice

Required separation at metering fixes achieved by: Conventional approach-

  • 1. Vectoring, holding
  • 2. Speed control

OPD-

  • 1. Speed control

The locations of these metering points are mostly based on expert

  • pinions or general

conventions.

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6 Optimal Metering Policies for OPD

Motivation: Proposed OPD Policies

  • OPD capability added to around 30 airports in U.S., 50

airports in Europe

  • Many airlines are using or collaborating in development
  • f OPD procedures
  • Chen and Solak (2014) provided a stochastic dynamic

framework to identify spacing and sequencing rules for OPD

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7 Optimal Metering Policies for OPD

Motivation: Proposed OPD Policies

If 𝑑𝑒 is the observed spacing at metering point 𝑒 between two flights, an optimal target spacing change Δ𝑒 at metering 𝑒 for the trailing aircraft is given as Δ𝑒 = 𝑛𝑒𝑑𝑒 + π‘œπ‘’, with parameters precalculated, e.g.

B738 trailing A320

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8 Optimal Metering Policies for OPD

Motivation: Proposed OPD Policies

  • If OPD is fully implemented (at the same rate as LAX)
  • Potential annual environmental savings: $5 million
  • Potential annual fuel burn savings: $24 million
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9 Optimal Metering Policies for OPD

Problem Framework: Research Questions

  • Currently no specific method being used to determine number

and locations of metering points for OPD.

  • Cost structure and trajectory variance are functions of distances

between metering points

  • Hence: Are there values in optimizing metering point locations?
  • What are the optimal number and locations of metering points?
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10 10 Optimal Metering Policies for OPD

Problem Framework

  • The number and locations of metering points are

determined, which will apply to all arriving aircraft.

  • Stochastic decision problem due to random trajectory

deviations

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11 11 Optimal Metering Policies for OPD

Problem Framework

  • A multi-stage decision structure:
  • First, the number and location decisions are made.
  • Then, spacing adjustment at the selected metering point

locations based on observed spacings are made.

  • Objective: Minimize expected costs associated with

maneuvering and runway utilization

  • Challenge: The ideal location for each type of aircraft

will be different; need to account for all types of aircraft in identified solutions

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12 12 Optimal Metering Policies for OPD

Problem Framework

  • Input
  • Arrival rate
  • Flight mixes
  • Location of top of descent
  • Distribution of trajectory deviation
  • Cost structure: fuel burn, runway utilization, cost of violation
  • f minimal spacing required
  • Output:
  • Strategic: number and locations of metering points
  • Tactical: the spacing adjustments for each arriving aircraft
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13 13 Optimal Metering Policies for OPD

Problem Framework

  • Multi phase algorithmic framework
  • Sequential use of Markov Decision Processes (MDP) and

Stochastic Programming (SP)

  • Phase I: Find the ideal number of metering points
  • Phase II: Based on Phase I solutions, identify the optimal

locations

  • Endogenous structure:
  • The number of metering points determines number of epochs

for spacing change in the model.

  • The locations determine the dynamics of trajectory deviation.
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14 14 Optimal Metering Policies for OPD

Algorithmic Design: Phase I - Optimal Number of Metering Points

Idea: Iteratively search for the estimated

  • ptimal number of metering points using

MDP model of Chen and Solak (2014) Assumption: Equal spacings in between Based on the spreadsheet tools, savings for each pair of aircraft are

  • btained.

If the addition of one more metering fix does not add value, stop.

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15 15 Optimal Metering Policies for OPD

Algorithmic Design: Phase II - Optimal Locations of Metering Points

When the number of metering points is known, a multi-stage stochastic programming model can be built.

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16 16 Optimal Metering Policies for OPD

Stochastic Programming Model

  • With the number of metering points fixed, the location

problem is a multistage decision model.

  • The decision timeline for the SP
  • Each possible realization πœ” ∈ Ξ¨, with probability π‘žπœ”
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17 17 Optimal Metering Policies for OPD

SP: Transition Dynamics

  • Transition dynamics 𝑄(𝑑𝑒+1|𝑑𝑒, Δ𝑒) modeling deviation

in trajectories

  • Calculation motivated by the analysis of Ren (2007)
  • Follows normal distribution
  • Mean and variance are functions of current spacing, target

spacing and distance between metering points

  • Defined as
  • 𝑂 (πœˆπ‘’+1 , πœπ‘’+1) where
  • πœˆπ‘’+1 = Δ𝑒 + 𝑑𝑒 + 𝑕𝑒 𝑑𝑒, 𝑒𝑒

= Δ𝑒 + π‘žπ‘’π‘‘π‘’ + π‘Ÿπ‘’π‘’π‘’ + 𝑠

𝑒;

  • πœπ‘’+1 = β„Žπ‘’ 𝑒𝑒 = πœƒπ‘’π‘’π‘’ + πœŠπ‘’;
  • 𝑒𝑒 is the distance between

metering points 𝑒 and 𝑒 + 1

Ref: (Ren 2007)

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18 18 Optimal Metering Policies for OPD

SP: Cost Structure

  • Fuel burn cost
  • Cost of maneuvering to achieve target spacing change Δ𝑒 at

next metering point for current spacing

  • Different parameters for each aircraft type and flight level
  • Two different flight phases (BADA)
  • Cruise fuel burn cost
  • Descent fuel burn cost

Where 𝑨𝑒 = 𝑒𝑒 + Δ𝑒, and 𝑧𝑒 is the location of metering point 𝑒.

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19 19 Optimal Metering Policies for OPD

SP: Cost Structure

  • Costs for violation of minimum spacing
  • Evaluated based on the probability of a collision (Blom et al.

2011)

  • Final spacing costs based on utilization of runway and

determined according to differences from minimum required spacing levels at runway

  • As calculated by Solveling et al.(2010)

Final spacing(nm) Cost($)

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20 20 Optimal Metering Policies for OPD

SP Formulation

  • Nonlinear Nonconvex Multistage Stochastic Program
  • The fuel burn cost functions 𝑔

𝑑𝑠, 𝑔 π‘œπ‘π‘› and 𝑔 π‘›π‘—π‘œ are nonconvex

  • Constraint (7) represents the dynamics due to spacing

change at each metering point.

𝑑

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21 21 Optimal Metering Policies for OPD

SP: Convex Representation

  • Objectives can be written using several bilinear terms
  • Let 𝑄𝑒 = 𝑑4 + 𝑑2𝑧𝑒 4.26, 𝑅𝑒 = 𝑨𝑒 + 𝑑1 𝑨𝑒

2 /𝑒𝑒, 𝑆𝑒 = 1 𝑑4+𝑑2𝑧𝑒 4.26𝑨𝑒

2 and π‘Š

𝑒 = ( 𝑒𝑒

4

𝑨𝑒 + 𝑑1𝑒𝑒 3), then, cruise stage

cost can be written as: 𝑔

𝑑𝑠 = 𝑑0𝑄𝑒𝑅𝑒 + 𝑑3π‘†π‘’π‘Š 𝑒

  • 𝑄𝑒, 𝑅𝑒, 𝑆𝑒, π‘Š

𝑒 are convex functions

  • Let π‘Œπ‘’ = 𝑒2/𝑨𝑒 + 𝑑12 𝑒𝑒 , 𝑋

𝑒 = 𝑑5 + 𝑑6𝑧𝑒 + 𝑑7 𝑧𝑒 2 +

𝑑8 𝑧𝑒

3 , 𝐺 𝑒 = 𝑑9 + 𝑑10𝑧𝑒 and 𝐻𝑒 = 𝑒𝑒 2/𝑨𝑒 . Thus, the

descent stage fuel burn cost functions can be written as: 𝑔

𝑒 = 𝑛𝑏𝑦{𝐺 𝑒𝐻𝑒, 𝑑11π‘Œπ‘’π‘‹ 𝑒}

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22 22 Optimal Metering Policies for OPD

SP: Convex Representation

  • Approximation of bilinear terms by piecewise linearization; e.g.

for 𝑄𝑒𝑅𝑒

πœ”

  • Two dimensional grid where the axes are over 𝑄𝑒 and 𝑅𝑒
  • Other bilinear terms are similarly approximated.

𝑄𝑒 𝑅𝑒

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23 23 Optimal Metering Policies for OPD

SP: Lagrangian Decomposition

  • Difficult to solve directly when the number of metering

points is greater than five. (2^5 scenarios, 100 pairs)

  • A Lagrangian function is generated by adding a set of

constraints to the objective function.

  • This allows for decomposition of the original problem

into sub-problems for each scenario

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24 24 Optimal Metering Policies for OPD

SP: Lagrangian Decomposition

Algorithm:

  • Step 1: For πœ” = 1, … , |Ξ¨| : Solve the decomposed minimization

problem for each scenario. Let π‘Š

πœ” and Ξ”πœ” be the objective value

and solution for each sub-problem, respectively.

  • Step 2: Let π‘Š

𝑀 = πœ” π‘Š πœ” , which is the optimal solution for the

Lagrangian dual.

  • Step 3: Let πœ”π‘”π‘¦ = argminπœ”{π‘Š

πœ”}. If there are multiple, select the

  • ne with the smallest index. Let Δ𝑔𝑦 = Ξ” πœ”π‘”π‘¦, which corresponds

to the scenarios that yield the smallest objective value.

  • Step 4: Let Ξ”πœ” = Δ𝑔𝑦; compute the corresponding objective

values π‘Š

π‘‰πœ” for each sub-problem. Let π‘Š 𝑉 = πœ” π‘Š π‘‰πœ” .

  • Step 5: If

π‘Šπ‘‰βˆ’π‘Šπ‘€ π‘Šπ‘€

≀ πœ—, stop; else, update the multiplier in the Lagrangian function and go to Step 1.

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25 25 Optimal Metering Policies for OPD

Implementation: Case Study

  • Hartsfield-Jackson Atlanta International Airport (ATL)

is selected as a representative major airport

  • The distance from TOD to runway is assumed to be

150 nm

  • Three different arrival rates, namely 20, 30 and 40

flights/hr are considered.

  • Flight mixes are generated based on historical

statistics of U.S. flights in 2012.

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26 26 Optimal Metering Policies for OPD

Implementation: Case Study

  • Phase I: Increase the number until the marginal

savings are sufficiently small (<1%).

  • Optimal number of metering points: 8
  • 20 flights/hr: 7 metering points
  • 30 and 40 flights/hr: 8 metering points
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27 27 Optimal Metering Policies for OPD

Implementation: Case Study

  • Phase II: Given 8 metering points, 256 (2^8) scenarios
  • The first 5 metering points are more closely distributed; the

remaining 3 have larger distances.

  • Higher altitude traffic control is more beneficial to OPD.
  • This configuration results in an increased savings of around

$23/flight, when compared with the current configuration. These savings imply a potential value of $3.8 million/year at ATL, given the assumption that adding a metering point incurs no direct costs.

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28 28 Optimal Metering Policies for OPD

Conclusions and Future Work

  • Identified the optimal number and locations for

metering points under an OPD setting

  • Adds to the values of OPD implementation; can be

extended to other metering procedures other than OPD

  • Sensitivity of these saving values over different airport

setups are being analyzed

  • Both general and specific insights for airports can be

derived

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Thank you…

Heng Chen heng@som.umass.edu