Technical Sessions – Monday July 11

TC-21 IFORS 2011 - Melbourne
Turkey, cavusogluo@itu.edu.tr, Mustafa Canolca, Demet
Bayraktar
The aim of study is to propose a selection system, will provide a comprehensive
approach for selecting the best GSM operator for Call Center, is selling GSM
operator’s prepaid minutes, investment. For this purpose, a literature review
is performed about GSM. Then, interviews have been carried out with authorized
experts; decision factors have been defined and evaluated by using AHP
respectively. Finally, the best alternative GSM operator has been selected by
using Goal Programming to consider cost, investment, profit etc. goals. The
results and future work have been discussed in detail.
� TC-21
Tuesday, 15:00-16:30
Meeting Room 218
Robustness and Recovery in Airline
Operations
Stream: Airline Applications
Invited session
Chair: Karla Hoffman, Department of Systems Engineering and
Operations Research, George Mason University, Mail Stop 4A6,
4400 University Drive, 22030, Fairfax, Virginia, United States,
khoffman@gmu.edu
1 - Robust Airline Scheduling: Minimising Propagated Delay
in an Integrated Routing and Crewing Framework
Michelle Dunbar, Mathematics and Statistics Dept., 18 Kalkada
Avenue, Gymea Bay, 2227, Sydney, NSW, Australia,
m.dunbar@unsw.edu.au, Gary Froyland
Traditionally, the airline scheduling problem has been sequentially decomposed
into various stages, with earlier decisions imposed upon those of subsequent
stages. Unfortunately, this fails to capture the dependencies between the stages
of aircraft routing and crew pairing, and how these dependencies affect the
propagation of delays through the flight network. To produce a jointly robust
solution, routing and crewing decisions need to be made together. We outline
a new approach to accurately calculate and minimise the cost of propagated
delay, in an integrated routing and crewing framework.
2 - Recoverable Robustness Approach for the Tail Assignment
Problem
Stephen Maher, Mathematics and Statistics, University of New
South Wales, 2052, Sydney, NSW, Australia,
stephen.maher@student.unsw.edu.au, Gary Froyland
Airlines are affected by disruptions daily and the recovery process can result
in increased operational costs. To reduce the difference from planned to operational
costs, redundancies are added using robust planning. In developing a
less conservative robust planning, we introduce the concept of recoverable robustness
for the tail assignment problem. We simultaneously solve the tail assignment
planning and recovery problems to integrate recovery decisions into
the original plan. As a result we develop a tail assignment that is recoverable
with a minimal number of operational changes.
3 - A Disruption Neighbourhood Approach to the Airline
Recovery Problem
Imran Ishrat, Engineering Science, The University of Auckland,
70 Symonds St, Level 3, Auckland, New Zealand,
i.ishrat@auckland.ac.nz, Matthias Ehrgott, David Ryan
Airlines plan their crew schedules and aircraft routes ahead of time. However,
on the day of operation schedules do not always proceed as planned due to
unforeseen disruptions. In such situations schedule recovery is desired to get
back to the originally planned schedule as soon as possible. In this work we
propose the idea of an expanding disruption neighbourhood to solve a sequence
of small crew rostering and aircraft routing problems (using set partitioning
models) until a suitable recovery solution is obtained. The method is tested on
various instances of disruptions on domestic operations of an airline.
4 - Cost of Delay to the Airline Industry
58
Karla Hoffman, Department of Systems Engineering and
Operations Research, George Mason University, Mail Stop 4A6,
4400 University Drive, 22030, Fairfax, Virginia, United States,
khoffman@gmu.edu
Establishing an accurate mechanism for estimating the cost of a delays for each
portion of a flight (gate costs, taxiing in and out costs, and en-route costs) is
useful for many aspects of modeling airline behavior and for better understanding
the likely impact of regulations. This paper explains how we used a Euro
Control paper (2004) and reversed engineer the components of their model to
be able to explicitly identify each of the components of an airline’s costs at
each segment of an airline flight: gate costs, taxiing costs, and airborne costs.