1 An Integer Linear Programming model for Airline Use (in the context of CDM) Intro to Air Traffic Control Dr. Lance Sherry Ref: Exploiting the Opportunities.

Slides:



Advertisements
Similar presentations
Federal Aviation Administration 1 Collaborative Decision Making Module 2 Developing A Collaborative Framework.
Advertisements

ATFM Priorities Evaluation Study Alison Hudgell QinetiQ
Vastly Distributed System ATCSCC CDM net TFM hub TRACONs TRACON(s) Towers ARTCC ARTCC(s) Towers Airports Industry AOC(s) GA International.
1 Market-based DEMAND MANAGEMENT strategies Giovanni Andreatta, University of Padova Lorenzo Brunetta, Milan Polytechnic Un. Guglielmo Guastalla, Eurocontrol.
CHARLES N. GLOVER MICHAEL O. BALL DAVID J. LOVELL Collaborative Approaches to the Application of Enroute Traffic Flow Management Optimization Models.
- European CDM - To benefit from the animation settings contained within this presentation we suggest you view using the slide show option. To start the.
Airline Schedule Optimization (Fleet Assignment I)
Collaborative Gate Allocation Alex Cuevas, Joanna Ji, Mattan Mansoor, Katie McLaughlin, Joshua Sachse, and Amir Shushtarian.
Evaluation of an Auction Mechanism for Allocating Airport Arrival Slots Eric J. Cholankeril William Hall John-Paul Clarke June 5, 2003.
1 The ATM Airport: VPI / VCI Switching Explained Carey Williamson Department of Computer Science University of Calgary.
Airport Collaborative Decision Making (A-CDM) Saulo Da Silva
M I T I n t e r n a t i o n a l C e n t e r f o r A i r T r a n s p o r t a t i o n New Approaches to Improving the Robustness of Airline Schedules Prof.
Efficiency and Equity Tradeoffs in Rationing Airport Arrival Slots Preliminary Results Taryn Butler Robert Hoffman, Ph.D.
LMI Airline Responses to NAS Capacity Constraints Peter Kostiuk Logistics Management Institute National Airspace System Resource.
Airlines and Linear Programming (and other stuff) Dr. Ron Lembke.
Service Network Design: Applications in Transportation and Logistics Professor Cynthia Barnhart Center for Transportation and Logistics Operations Research.
Airlines and Linear Programming Dr. Ron Tibben-Lembke.
NAS Resource Allocation: Economics and Equity Summary Observations Wye Woods Conference Center March 19, George Mason University University of.
1 A Second Stage Network Recourse Problem in Stochastic Airline Crew Scheduling Joyce W. Yen University of Michigan John R. Birge Northwestern University.
Airspace Resource Allocation -Operations Impact Prof. R. John Hansman, Director MIT International Center for Air Transportation
New Approaches to Add Robustness into Airline Schedules Shan Lan, Cindy Barnhart and John-Paul Clarke Center for Transportation and Logistics Massachusetts.
SCHEDULING AIRCRAFT LANDING Mike Gerson Albina Shapiro.
Users’ Perspective of PBN Achieving Improvements Joël Morin Head, ATM Harmonization.
Airline Schedule Planning: Accomplishments and Opportunities Airline Schedule Planning: Accomplishments and Opportunities C. Barnhart and A. Cohn, 2004.
Quadratic Programming Model for Optimizing Demand-responsive Transit Timetables Huimin Niu Professor and Dean of Traffic and Transportation School Lanzhou.
Gate-to-Gate Project: Implementing Sequencing, Merging, and Spacing Captain Bob Hilb September 11, 2006.
Date: 18 February 2008 Federal Aviation Administration Collaborative Decision Making at the FAA/ATO A look at how CDM is applied in the U.S.
Location Models For Airline Hubs Behaving as M/D/C Queues By: Shuxing Cheng Yi-Chieh Han Emile White.
Route Planning and Evaluation
EUROCONTROL EXPERIMENTAL CENTRE from Passenger Perspective or… I n t e r m o d a l i t y from Passenger Perspective or… PhD Thesis EUROCONTROL Experimental.
International Civil Aviation Organization Aviation System Block Upgrades Module N° B0-35/PIA3 Improved Flow Performance through Planning based on a Network-Wide.
Content : 1. Introduction 2. Traffic forecasting and traffic allocation ◦ 2.1. Traffic forecasting ◦ 2.2. Traffic allocation 3. Fleeting problems ◦ 3.1.
„ Fuzzy Expert” System for Determination of Runways in Use Case Study: Zurich Airport Fedja Netjasov University of Belgrade Faculty of Traffic and Transport.
Workshop on National Airspace System Resource Allocation: Economics and Equity Organizers: Mike Ball, University of Maryland George Donohue, Karla Hoffman,
1 Botond Kovari: Crew Planning 1 st Int. Conf. on Research in Air Transportation - Zilina, Nov 22-24, 2004 Cost Optimisation Methods in Air Crew Planning.
MIT ICAT ICATMIT M I T I n t e r n a t i o n a l C e n t e r f o r A i r T r a n s p o r t a t i o n Virtual Hubs: A Case Study Michelle Karow
Workshop PRIXNET – 11/12 Mars CONGESTION PRICING IN AIR TRANSPORTATION Karine Deschinkel Laboratoire PRiSM – Université de Versailles.
LMINET2: An Enhanced LMINET Dou Long, Shahab Hasan December 10, 2008.
Hub Location Problems Chapter 12
ASIA PACIFIC Air Traffic Flow Management
Airlines and Linear Programming (and other stuff) Dr. Ron Lembke.
Heuristic is a technique designed to solve a problem that ignores whether the solution can be proven to be correct, but which usually produces a good solution.
CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH COLLABORATIVE DECISION MAKING (CDM) Testimony before the National Civil Aviation Review Commission Testimony.
Route and Network Planning
1 REAL-TIME INTER-MODAL SUBSTITUTION (RTIMS) AS AN AIRPORT CONGESTION MANAGEMENT STRATEGY Mark Hansen Yu Zhang University of California Berkeley.
Benefits of CDM Within AFI Region Presented by: Mikateko Chabani.
COLLABORATIVE DECISION MAKING
How do we mitigate weather impacts to the NAS?
14/01/20161 Air Traffic Management Panel Madrid May 2002 AVIATION OPERATIONAL MEASURES FOR FUEL AND EMISSIONS REDUCTION WORKSHOP HOW TO SAVE FUEL.
Federal Aviation Administration 1 Collaborative Decision Making Module 5 “The Collaborative Environment”
INAIR 2015: BOOK PRESENTATION Miguel Mujica Mota Daniel Guimerans Serrano Geert Boosten.
RSPA/Volpe Center Arrival/Departure Tradeoff Optimization at STL: a Case Study Dr. Eugene P. Gilbo tel.: (617) CDM.
Analysis of Demand Uncertainty in Ground Delay Programs Mike Ball University of Maryland.
Linear Programming Wyndor Glass Co. 3 plants 2 new products –Product 1: glass door with aluminum framing –Product 2: 4x6 foot wood frame window.
University of Brasilia TransLab Enhancement of Airport Collaborative Decision Making with Matching Theory Antonio Carlos de Arruda Junior Li Weigang Kamila.
1 A General Approach to Equity in Traffic Flow Management and Its Application to Mitigating Exemption Bias in Ground Delay Programs Michael Ball University.
Simplifying Travel How We Manufacture Time, Increase Productivity and Reduce Costs An educational presentation on why businesses and individuals use private.
RSPA/Volpe Center Arrival/Departure Capacity Tradeoff Optimization: a Case Study at the St. Louis Lambert International Airport (STL) Dr. Eugene P. Gilbo.
Airport Gate Scheduling
Airport Collaborative Decision Making (A-CDM) Saulo Da Silva
Le Jiang (IMSG) and Frank Brody (NWS) (August 2, 2016)
Collaborative Decision Making Module 5 “The Collaborative Environment”
Users’ Perspective of PBN Achieving Improvements
The Role of Air Traffic Control in A CDM ENVIRONMENT
Airport Slot Coordination & its Role in the CDM Process
Workshop on preparations for ANConf/12 − ASBU methodology
Airport Collaborative Decision Making (A-CDM) Saulo Da Silva
Workshop on preparations for ANConf/12 − ASBU methodology
Airlines and Linear Programming (and other stuff)
Collaborative Decision Making “Developing A Collaborative Framework”
Presentation transcript:

1 An Integer Linear Programming model for Airline Use (in the context of CDM) Intro to Air Traffic Control Dr. Lance Sherry Ref: Exploiting the Opportunities of Collaborative Decision Making: A model and Efficient Solution Algorithm for Airline Use, Paul M. Carlson, Transportation Science, vol34, No. 4, Nov Briefing by: Babak. G. Jeddi Feb 2005

2 An IP model for Airline Use in the context of CDM A term Arrival Capacity: max # of possible landings without safety reduction Improving traffic flow mgt when weather condition reduces an airport’s Arrival Capacity During periods of reduced capacity (undersupply), FAA’s role shifts (should shift) from centralized DM to information gather and resource intermediary/negotiator Seeking Collaboration between FAA and Airlines CDM: A partially-decentralized Air Traffic Mgt (ATM) environment in which the airlines have increased decision making authority

3 The Problem Hubbing: the process of routing-origin destination traffic through a connecting airport rather than serving it non-stop! Requires careful scheduling to balance the conflicting goals of linking the greatest # of city pairs while minimizing the time that passengers spend at the hub. Many flights arrive within a short interval time (arrival bank), and many flights (departure bank) are scheduled to depart in a short time. Problem: schedules are very sensitive to disruption, due to the existence of connection dependencies. Hubbing airlines are often disrupted by weather and other factors! In 1996, disrupted flights caused loss of $440 million for Delta Airlines So the problem is very serious!

4 Solution! Question: Which flight should land first (prioritization)? A term Arrival slot: a time window during which the holder of the slot has permission to land one aircraft. FAA regulates the use of the scarce arrival capacity by allocating arrival slots among the airlines; Ground Delay Program, CDM, etc CDM has been replacing Ground Delay Program CDM: FAA informs the airlines about the state of the system and allocate constrained resources among the airlines; each airline will then decide/manage own operations according to its own priorities and objectives. FAA estimates that CDM would save about $2.6 billion over eight years (CDM homepage, 1997).

5 The airline must assign its inbound flights to its limited arrival slots, canceling or significantly delaying those flights without a slot. Also, decide whether to delay the completion times of inbound banks and by how much (called “spreading the bank”), which will determine how many of its bank flights will remain in their bank and how many will be separated. Using flight cancellation, flight separation, flight delay, and bank spread costs, the airlines seek the lowest cost solution among all the set of feasible solutions to the problem.

6 An OR (IP) model ! Objective function: – minimize the total cost of changes to scheduled operations, i.e. min sum of the costs of bank spread, flight delay, flight cancellation, and flight separation Constraints: – All banks should be eventually completed – All the flights in the bank to have either arrived, been canceled, or been separated – All flights are either arrived or cancelled – Arrival slot capacity restriction – …

7 A model for Airline Use in the context of CDM An Integer Linear Programming Model (by P. Carlson 2000): Decision variables:

8 A model for Airline Use in the context of CDM

9 Final point Model can be solved in real time (2 minutes)

10 Comments and Questions, please!