2016 INFORMS Annual Meeting Program

503 / 561

INFORMS Nashville – 2016
503
WE62
Cumberland 4- Omni
Passengers and Parts: Analytics and Machine
Learning in Aviation
Sponsored: Aviation Applications
Sponsored Session
Chair: Catherine Cleophas, RWTH Aachen University, Kackertstr. 7,
Aachen, 52072, Germany,
Co-Chair: Phillip Mah, Boeing, Commerce, Richmond, BC, V6V 2L1,
Canada,
1 - Predictive Maintenance from Analysis Of Airplane Sensor Data
Phillip Mah, Boning, Commerce, Richmond, BC, V6V 2L1,
Canada, Ruiwei Jiang,
Dawen Nozdryn-Plotnicki
Unscheduled maintenance drives 10% of the annual operational cost to airlines
worldwide. Predictive Maintenance could reduce those costs, particularly when
synchronized with airline’s operations. By using engineering expertise, statistics
and machine learning on aircraft sensor and fault data, as well as analysis of an
airline’s flight and maintenance schedule, we detect impending issues on the
aircraft and suggest maintenance tasks in accordance with the prediction and an
airline’s working rhythm. These predictive maintenance tasks will increase
reliability and reduce unscheduled maintenance.
2 - Applications Of Text Mining Techniques To Fleet Health And
Maintenance Data
Bingjing Yu, Boeing Vancouver, 1146 Homer Street, Vancouver,
BC, V6B 2X6, Canada, Gaku Tobinobu,
Candice Chan, Ehsan Nobakht
Textual data is one of the richest data sources for fleet health and maintenance
analytics. Taking advantage of these information is the key for optimizing airline’s
and Boeing’s business. Due to its large volume and highly unstructured nature,
however, its full potential is rarely leveraged. Advanced Analytics group works on
text analytics projects with airlines in the areas such as reliability, maintenance
program and Boeing customer support. Case studies on how we help businesses
by applying natural language processing, machine learning and visualization
techniques will be presented.
3 - On The Flight Choice Behaviour Of Business Purpose Passengers
In The Australian Domestic Market
Cheng-Lung Wu, Associate Professor, UNSW Australia, School of
Aviation, UNSW Australia, Kensington, NSW 2052, Australia,
, Hanson So
This paper examined the flight choice behaviour difference of business-purpose
passengers who work in small and medium enterprises (SMEs,) and those in non-
SMEs. Statistics show that SME business passengers tend to fly less, are more
price-sensitive, and derive less satisfaction in flying with full-service carriers if
they have previously flown with low-cost carriers. Discrete choice models show
that fewer flight service attributes are significant on shorter flights. Flight comfort
attributes have a larger significance on inbound flights. Attribute non-attendance
(ANA) is above 55% for all tested attributes except fare; not all attributes are
perceived equally by business passengers.
WE63
Cumberland 5- Omni
Urban Operations Research
Sponsored: Location Analysis
Sponsored Session
Chair: Daisuke Watanabe, Tokyo University of Marine Science and
Technology, 2-1-6 Etchujima, Koto-ku, Tokyo, 135-8533, Japan,
1 - Coverage Modeling In Public Street Lighting
Alan T. Murray, University of California at Santa Barbara, Santa
Barbara, CA, 93106, United States, Xin Feng
The spatial distribution of public area lighting in an urban region greatly
influences human activities and safety, yet is costly to provide. This paper details a
coverage optimization model for nighttime light provision, enabling benefits and
impacts to be taken into account. Application results for an urban area are
presented and discussed.
2 - A Robust Optimization Approach For Ambulance
Location Problem
Hozumi Morohosi, National Graduate Institute for Policy Studies,
, Takehiro Furuta
This work studies an application of robust optimization to a cooperative covering
model with focusing on ambulance location problem. We propose a procedure for
defining the uncertain set of parameters in the problem based on actual data in a
Bayesian way. Then we bring out a robust version of cooperative covering
problem and give a solution analysis with some numerical examples.
3 - Generalized Weighted Benefit And Maximal Expected Covering
Location Problem
Daisuke Watanabe, Tokyo University of Marine Science and
Technology, Richard Church
The purpose of this study is to analyze the optimal location model for Counter-
Piracy Surveillance System in Somalia using Weighted Benefit and Expected
Coverage Model based on Maximal Covering Location Problem.
WE64
Cumberland 6- Omni
Vector Optimization: Algorithms and Applications
Sponsored: Multiple Criteria Decision Making
Sponsored Session
Chair: Firdevs Ulus, Bilkent University, Ankara, Turkey,
1 - Multiobjective Risk-averse Two-stage Stochastic Programming
Cagin Ararat, Assistant Professor, Bilkent University, Ankara,
06800, Turkey, , Ozlem Cavus
Risk-averse two-stage stochastic programming is concerned with the
minimization of a risk measure of a random cost function over the feasible
choices of a deterministic and a random decision variable. We study the
multiobjective version of this problem in which case the cost function is vector-
valued and its risk is quantified via a set-valued risk measure. Although the
resulting problem has a set-valued objective function, we reformulate it as a
convex vector optimization problem and propose a customized version of
Benson’s algorithm to solve it. In particular, we develop duality-based cutting-
plane type methods to solve the scalar subproblems appearing in Benson’s
algorithm.
3 - Convex Vector Optimization Problems: Unboundedness
Firdevs Ulus, Bilkent University,
In order to solve convex vector optimization problems (CVOPs), Benson type
algorithms have been proposed recently. These algorithms work well if the
feasible region is compact which implies that the problem is bounded and a ‘finite
weak epsilon-solution’ to the problem exists. However, in many applications, the
feasible region of the problem does not necessarily compact and the problem may
be unbounded. In this talk, the aim is to discuss the following: 1- Is there a finite
solution concept for unbounded convex optimization problems? 2- If this is the
case, are there conditions which guarantee the existence of such a solution? 3-
Can we extend the existing algorithms to unbounded CVOPs?
2 - Quantification Of The Robustness Gap For Uncertain
Multiobjective Optimization Problems
Corinna Krüger, Georg August University Göttingen, Göttingen,
Germany, , Anita Schöbel,
Margaret M Wiecek
We investigate linear multiobjective optimization problems with uncertain right
hand sides of the constraints which are modeled as the elements of a polyhedral
uncertainty set. Generalizing Ben-Tal and Nemirowski’s definition of the
robustness gap (RG) for the single objective case to the multiobjective case, we
quantify the gap in two ways. We develop a quadratic program whose objective
value corresponds to the RG. We also relate the RG to the distance beween
appropriate Pareto sets.
WE64