2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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5 - Prioritizing Hepatitis C Treatment in U.S. Prisons
Turgay Ayer, Georgia Tech, Atlanta, GA, Boston, MA,
, Anthony Bonifonte, Can Zhang, Anne
Spaulding, Jagpreet Chhatwal
High prevalence of HCV in prisons offers a unique opportunity to control the HCV
epidemic. Newest HCV treatments drugs are effective but providing treatment is
outrageously expensive. We propose a restless bandit modeling framework to
support hepatitis C treatment prioritization decisions in U.S. prisons. From the
interpretation of this closed-form expression, we anticipate the performance of
Whittle’s index would degrade as the treatment increases. Using a detailed agent-
based simulation model, we show our proposed policy can significantly improve
overall health outcomes compared with the current practice. Our results shed
light on issues in hepatitis C prioritization: 1) considering remaining sentence
length and injection drug use (IDU) status and liver health state in prioritization
decisions can lead to a performance improvement; 2) when linkage-to-care rate
outside prison is small while treatment capacity in prison system is relatively
large, patients with shorter remaining sentence lengths should be prioritized; and
3) for patients with advanced liver disease, IDUs should not be prioritized unless
their reinfection is very-well controlled.
SD70
Acoustic- Omni
Transportation, General
Contributed Session
1 - Solving The Privately Owned Automated Vehicles
Assignment Problem
Theresia van Essen, Delft University of Technology, Mekelweg 4,
Delft, 2628 CD, Netherlands,
Gonçalo Correia
We propose a new model to study how replacing privately owned non-automated
vehicles with shared automated ones affects travel time, congestion and parking
demand in an urban area. As automated vehicles will reduce the value of travel
time, it is expected that travel time will increase. In addition, congestion is on the
one hand expected to increase because of the empty trips, and on the other hand
expected to decrease because of a reduction in the number of vehicles on the
road. Parking demand is expected to decrease as the utilization of the vehicles will
increase. The model is applied to a case study based on the city of Delft, the
Netherlands.
2 - Developing Interrelated Airport Facilities under Uncertainty:
A Network Flow Formulation
Yanshuo Sun, University of Maryland, 1173 Glenn Martin Hall,
College Park, MD, 20742, United States,
Paul Schonfeld
Interactions between user flows and facilities are quite complex in an airport
system. Thus, capacity expansion decisions for these facilities are largely
interrelated. A network flow formulation is proposed for coordinating such
development decisions so that a balanced capacity configuration is likely to be
obtained. The nonlinear congestion effect, which is common in most airport
facilities, is considered and uncertainties in demand and aircraft mix are also
included. The stochastic mixed integer nonlinear program is reduced to a
deterministic mixed integer program and thus solved.
3 - From Trend Spotting To Trend Setting: Modeling The Impact
Of Major Technological And Infrastructural Changes On
Travel Demand
Feras El Zarwi, PhD Candidate, University of California at
Berkeley, 2100 Channing Way, Apt 415, Berkeley, CA, 94704,
United States,
Transformative mobility will revolutionize travel and activity behavior but we
should be cautious with how the future is going to play out. This research project
proposes a methodological framework tailored to address impacts of technological
innovation to understand and predict long-range trends in travel behavior. We
integrate hidden markov and discrete choice models to predict long-range trends
in travel behavior as a result of adopting new services. The model is estimated on
a longitudinal travel diary dataset from Santiago, Chile. The proposed quantitative
methods are critical in assessing how policies/strategies can influence trends of
travel behavior to guide transformative mobility.
4 - Using Regression Tree Models To Improve Freeway Incident
Duration Prediction A Comprehensive Case Study In
Maryland Region
Xuechi Zhang, Graduate Research Assistant, University of
Maryland, 0147C Engineering Lab Building, College Park, MD,
20740, United States, , Ali Haghani,
Yeming Hao
Timely and accurate prediction of freeway incident duration is not only useful for
providing travelers with re-routing strategies, but can also reduce their in-vehicle
anxiety. This research proposed several regression tree based models to improve
the incident duration prediction accuracy by fusing heterogeneous information,
i.e. incident information, weather and traffic conditions. A comprehensive case
study with real-world data in Maryland Region was conducted to evaluate and
demonstrate the proposed models. Further, practical implications from the case
study were given.
SD71
Electric- Omni
Transportation, Rail
Contributed Session
Chair: Emmanuel Martey, University of Delaware, 302 DuPont Hall,
Newark, DE, 19716, United States,
1 - Optimization Techniques For Railways
Srinivasa Prasanna, Professor, IIIT-Bangalore, 26/C, Hosur Road,
Electronics City, Opposite Infosys Technologies, Bangalore,
560100, India, ”
We present optimization techniques used in portions of the Indian Railway
System, the largest in the world. We present techniques used for timetabling and
investment planning, under large scale demand uncertainty. Many of these
problems are at the scale of grand computational challenges (with 10’000 of trains
and 1000’s of control points), and the talk will present a few pieces of how
portions of this problem can be simplified and made amenable to optimization
techniques (convex/non-convex). Exemplary results will be discussed.
2 - Railway Capacity Analysis And Cyclic, Combined Train
Timetabling And Platforming For A Single Track, Bidirectional
Railway Line
Matthew Petering, University of Wisconsin-Milwaukee, Industrial
and Manufacturing Engineering Dept, Ems E367, Milwaukee, WI,
53201, United States, , Mojtaba Heydar
We present the literature’s first mixed integer linear program for cyclic train
timetabling and platforming on a single track, bi-directional railway line. There
are T train types and one train of each type is dispatched per cycle. The decisions
to be made include the sequencing of the train types on the main line and the
assignment of train types to station platforms. Two conflicting objectives—
minimizing cycle length and minimizing total train journey time—are considered.
3 - A MIP Model For High-speed Train Platforming Problem With
Route Conflicts Constraints
Gongyuan Lu, Assistant Professor, Southwest Jiaotong University,
111#, 1st Section, Northern 2nd Ring Road, School of
Transportation and Logistics, Chengdu, China,
, Guangyuan Zhang, Yuan Wang
The biggest challenge in solving high-speed train platforming problem (HTPP) is
to route trains without conflicts. Especially in a large multi-yard railway station,
the conflicts between routes and platforms can easily make the scale increase
dramatically. A MIP model aiming at minimize train delay is formulated to
generate flexible train schedule without violating route conflicts. This research
has been applied in largest high-speed train station in Asia.
4 - Predicting Cascading Effects Of Local Disruptions In A Large
Scale Rail Network
Patrick Briest, McKinsey & Co, Kennedydamm 24, Dusseldorf,
40027, Germany, , Sebastian Albert,
Robin Blöhm, Florian Brummer, Christian Gruß,
Eike-Dennis Rausch
We propose a stochastic simulation model to determine network-wide effects of
locally induced disruptions in a large scale rail system. We extend the model
previously described by Berger et al. to include (A) dynamic diversion routing to
mimic how traffic controllers will try to route trains around disrupted parts of the
network and (B) track capacities and a load-dependent delay component. We
present computational results based on detailed delay distributions extracted from
multiple years of Deutsche Bahn’s operations data and simulation runs using both
current and historic schedules of regional, long-distance and cargo traffic in the
Deutsche Bahn network.
SD70