2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
401
WB18
106A-MCC
DMA General
Contributed Session
Chair: Jose M. Merigo, Full Professor, University of Chile, Av. Diagonal
Paraguay 257, Santiago, 8330015, Chile,
1 - Cyber Attacker Choices In A Three-way Behavioral Security Game
Jinshu Cui, University of Southern California, 3620 McClintock
Avenue, # 501, Los Angeles, CA, 90089, United States,
, Richard S John, Heather Rosoff
This study focuses on cyber attackers’ choices in a security game involving
attackers, defenders, and users. An attacker can choose to attack the defenders or
the users. Deterrence is measured by the third option of the attacker - no attack.
Conversely, the defenders and users can select either a standard or enhanced
security level. We conducted a behavioral experiment in which 497 subjects
played as attackers over 30 rounds of the game and were incentivized based on
their performance. Defenders’ and users’ joint strategies were manipulated.
Results indicated that subjects were more likely to hack database when
EV(database) was higher, and were more deterred when both EVs were negative.
2 - Sequential Decisions Following Near Misses
Jinshu Cui, University of Southern California, 3620 McClintock
Avenue, # 501, Los Angeles, CA, 90089, United States,
, Richard S John
Prior near miss experiences have been identified as a contributing factor in
responses to risks of disasters. Researchers found a near miss event could lead
individuals to interpret the risk as either “vulnerable” or “resilient”, while had no
conclusions on what could lead to different interpretations. The current study
hypothesizes that responses to near miss events are determined by psychological
distance. We conducted a behavioral experiment in which 100 subjects were
exposed to a sequence of 20 events. Results indicated that subjects were less likely
to engage in protective measures when a near miss event is psychological distant
to the decision maker and when more near misses were experienced.
3 - Column Generation For Airline Crew Rostering: Practical
Considerations In A Production System
Andreas Westerlund, Optimization Expert, Jeppesen,
Odinsgatan 9, Gothenburg, 411 03, Sweden,
Jeppesen’s crew rostering optimizer is today used by around 40 airlines to
produce monthly schedules for their flying crew. The optimizer allows the user to
configure various kinds of business logic and it solves monthly schedules for
problem instances with above 20k crew-members and 100k activities.
In this presentation we will start by defining the rostering optimization problem
in general. Then we will describe our column generation framework that is used
to deal with it. Finally we will look at the specific problem of having an efficient
fixing process in the presence of high degree of symmetry.
4 - The Internet Of Things: Preliminary Research Results
Gary D Scudder, Vanderbilt University-OGSM,
401 21st Avenue South, Nashville, TN, 37203-2422, United States,
Sal March
In this research, we look at the emerging field of the Internet of Things and
develop a research agenda for managerial issues. In addition, we will discuss
research in IoT for preventive maintenance. IoT is shown to be beneficial in
reducing costs and increasing profits.
5 - The Ordered Weighted Average Division
Jose M. Merigo, Full Professor, University of Chile, Av. Diagonal
Paraguay 257, Santiago, 8330015, Chile,
Sigifredo Laengle, Ronald R Yager
The ordered weighted average division is an aggregation operator that aggregates
a set of divisions providing a parameterized family from the minimum to the
maximum division. The work considers a wide range of particular cases including
the average division, the median division and the weighted average division. It
also develops some further extensions including the weighted ordered weighted
average division and the generalized weighted ordered weighted average division.
This approach can be applied in a wide range of problems dealing with the
aggregation of divisions including decision making and computational
intelligence.
WB19
106B-MCC
Future of Disease Modeling in Clinical and
Public Health
Sponsored: Computing
Sponsored Session
Chair: Zeynep Ertem, University of Texas-Austin, University of Texas-
Austin, Austin, TX, United States,
1 - Pandemic Influenza Preparedness
David Morton, Northwestern University,
We describe three data-driven optimization models that inform resource
allocation in preparing for an influenza pandemic. In particular, we optimize: the
mix of central and regional stockpiles of ventilators, accounting for stochastic
peak-week demand; the spatial allocation of antivirals, considering under-insured
populations and hard-to-reach locations; and, the spatial allocation of multiple
types of vaccines with differing suitability for each prioritized target population.
We discuss challenges and extensions.
2 - Role Of Operations Research In Chronic Disease Management
Mariel Lavieri, University of Michigan,
I discuss past and future challenges encountered in managing chronic diseases.
3 - Mathematical Models for Cancer Screening
Fatih Safa Erenay, University of Waterloo,
My talk will provide an overview of models proposed for the optimal cancer
screening problem from societal and personal perspectives. I will start with the
classical models that schedule screening interventions over a planning horizon,
and describe the evolution of the literature towards more dynamic, partially
observable, and personalized models over examples from colorectal cancer
screening. The talk will also highlight the current challenges and recent trends in
cancer screening literature.
WB20
106C-MCC
Assets and Structured Hedges in Energy Markets
Severe Incompleteness and Methods for Dealing
with It
Invited: Tutorial
Invited Session
Chair: Glen Swindle, Scoville Risk Partners, 405 Lexington Avenue,
21st Floor, New York, NY, 112, United States,
1 - Assets And Structured Hedges In Energy Markets –
Severe Incompleteness And Methods For Dealing With It
Glen Swindle, Scoville Risk Partners, 405 Lexington Avenue,
21st Floor, New York, NY, 12, United States,
Risks in energy markets are inherently high dimensional due to large numbers of
delivery locations and physical attributes, stochastic demand, and seasonality. In
contrast, the number of instruments with sufficient liquidity to support hedging
activities is relatively small, and has never been able to span the set of risks
sustained by market participants. This mismatch has spawned an interesting and
arguably unique set of challenges related to the valuation and hedging of energy
portfolios. Here we will survey examples of such, including variable quantity
swaps, generation and structured asset hedges.
WB20