2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
170
MB62
Cumberland 4- Omni
Data Mining in Air Transportation
Sponsored: Aviation Applications
Sponsored Session
Chair: Yi Liu, University of California, 107 McLaughlin Hall, Berkeley,
CA, 94720, United States,
1 - Using Historical Data To Support Traffic Management
Initiative Decisions
Alexander Estes, University of Maryland-College Park, 3117 A.V.
Williams, University of Maryland-College Park, College Park, MD,
United States, , David J Lovell,
Michael O Ball
There is a large amount of data collected about traffic management initiatives that
have been taken in the past by the Federal Aviation Administration. While this
information could be helpful to decision-makers that are attempting to plan traffic
management initiatives, this data is currently not very accessible. We propose
unsupervised learning methods that identify relevant data and present it to the
decision-makers.
2 - Identifying Similar Days To Guide Traffic Management
Decision Making
Sreeta Gorripaty, UC Berkeley, Berkeley, CA, 94703, United States,
, Mark M Hansen
The experience of traffic flow management specialists is crucial in managing
airport operations efficiently. Historical airport operations data can assist decision-
making by augmenting controller experience with a systematic record of past
traffic management actions under similar conditions and their consequences. A
decision-support tool that finds historical days similar to a query day can guide
day-of-operation decisions and assess past performance. Using machine-learning
algorithms, we learn a similarity measure between two days based on weather
and demand data. This measure is assessed for accuracy using airport operational
outcomes and traffic management initiatives (TMI) data.
3 - Impacts Of Airline Mergers On Consumer Welfare
Vikrant Vaze, Assistant Professor, Dartmouth College,
14 Engineering Drive, Hanover, NH, 03755, United States,
, Tian Luo
We used publicly available passenger flows data and a discrete choice modeling
framework to examine welfare changes due to the five major airline mergers in
the past decade in the United States. We found that consolidations of legacy
airlines with significantly overlapping markets generally increased the passenger
welfare. However, overall passenger welfare in small communities declined after
the two mergers whose small community markets data sets are sufficiently large
for our analyses. We also found that welfare of passengers, traveling to or from
hub airports of the primary merging airline, increased significantly.
4 - Hourly Ground Delay Program Prediction With Local And
Convective Weather Variables
Mark M Hansen, University of California - Berkeley,
114 McLaughlin Hall, Berkeley, CA, 94720, United States,
Yi Liu, Danqing Zhang,
Alexey Pozdnukhov
We propose a method to predict ground delay program status for each hour using
data mining techniques. We use local and convective weather variables as our
predictors. We apply the method to 5 top-traffic US airports. The results include
prediction performance, variable importance analysis and convective weather
weight map.
MB63
Cumberland 5- Omni
HUB Location
Sponsored: Location Analysis
Sponsored Session
Chair: Sibel Alumur Alev, University of Waterloo,
200 University Avenue West, Waterloo, ON, N2L 3G1, Canada,
1 - Modeling Congestion And Service Time In Hub
Location Problems
Stefan Nickel, Karlsruhe Institute of Technology,
, Sibel Alumur Alev, Brita Rohrbeck,
Francisco Saldanha-da-Gama
In this paper, we present a modeling framework for hub location problems with a
service time limit considering congestion at hubs. Service time is modeled taking
the traveling time on the hub network as well as the handling time and the delay
caused by congestion at hubs into account. We develop mixed-integer linear
programming formulations for the single and multiple allocation versions of this
problem. We further extend the multiple allocation model with a possibility of
direct shipments. We test our models on the well-known AP data set and analyze
the effects of congestion and service time on costs and hub network design.
2 - An Enhanced Milp Model For Stochastic Multi-period Multiple
Allocation Hub Location
Francisco Saldanha-da-Gama, University of Lisbon, Lisbon,
Portugal,
Isabel Correia, Stefan Nickel
A two-stage stochastic programming modeling framework is proposed for a pure
phase-in multi-period multiple allocation hub location problem. Stochasticity is
associated with the demands. Assuming a finite support for the underlying
random vector, a compact formulation can be derived for the extensive form of
the deterministic equivalent, which leads to a large-scale mixed-integer linear
optimization problem. By considering 4 sets of valid inequalities, the model is
enhanced, which makes it possible to solve to optimality by means of a general
solver, instances that could not be tackled when the original formulation was
considered. Results obtained using the CAB data set are reported.
3 - Modeling Hub Location Problems
James F Campbell, University of Missouri-St Louis,
, Sibel Alumur Alev
In this talk, we focus on some of the key features of hub location models such as
demands, costs, economies of scale, capacity and service level constraints, and
network topologies including single and multiple allocation and complete and
incomplete inter-hub networks. We discuss some of the possible implications of
how these features are modeled on the hub locations, network design and
performance measures, and emphasize the characteristics within the context of
different applications. Additionally, we identify some distinguishing properties of
the CAB, AP, and TR data sets commonly used in hub location studies. We aim to
provide a road map for future hub location research.
MB64
Cumberland 6- Omni
MCDM for System Design: No Camels Allowed
Sponsored: Multiple Criteria Decision Making
Sponsored Session
Chair: Stephen Henry, Sandia National Laboratories, P.O. Box 5800,
Albuquerque, NM, 87185, United States,
1 - Multiple Criteria Decision Making For The United States Army’s
Robotic Pack Mule Design
Lucas Waddell, Sandia National Laboratories,
Stephen Henry
The U.S. Army has a strong interest in the development and deployment of
unmanned ground systems (UGSs) to provide soldiers with many unique
battlefield advantages. As with any new, complex system, UGSs present a vast
array of design tradeoffs, interdependencies, and competing stakeholder goals.
There is an extremely fine line between design solutions that represent fair
compromises, and solutions that nobody is willing to accept. We present lessons
learned from an in-depth trade study performed for the U.S. Army on the Squad
Multipurpose Equipment Transport (SMET) UGS.
2 - Infrastructure Equipment Optimization For United States Military
Contingency Base Designs
Alexander Dessanti, Sandia National Laboratories, Albuquerque,
NM, United States,
Contingency bases provide temporary facilities from which deployed forces can
operate overseas. These bases are large-scale complex systems with many
interrelated functions, making it difficult to identify the best equipment to utilize
when designing a new one without an optimization approach. The Whole System
Trades Analysis Tool (WSTAT), a multi-objective optimization capability, is being
applied to this problem for the U.S. Army to enable more efficient and affordable
future contingency base designs. Focus will be on the unique technical challenges
presented by a problem of this magnitude.
3 - Ultra-high Dimensional Optimization For Military Systems
Requirements Negotiation
Stephen Henry, Sandia National Laboratories,
Complex military system design begins long before the welding of metal. A key
early step is the development of a set of requirements - performance levels in
various categories that must be achieved by the new system. These requirements
are typically drafted by separate communities of experts and lack an analytic
mechanism to address incompatibilities between the individual requirements -
often leading to program cancellation due to unmet requirements. In this talk, we
present a new tool for ensuring holistically feasible requirements. We discuss the
mathematical challenges of high-dimensional optimization (>30 objectives) as
well as the human challenges of real-time requirements negotiation.
MB62