Informs 2015

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INFORMS Philadelphia – 2015
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71-Room 202B, CC
Transportation Operations II
Contributed Session
Chair: Ioannis Akrotirianakis, Siemens Corporate Technology,
750 College Road, Princeton, NJ, United States of America,
1 - Optimization of Area Traffic Control: A Binary Mixed Integer Linear
Programming Approach
Zhao Zhang, Researcher Assistant, Tsinghua University,
Room 615, Shude Bbuilding, Beijing, 100084, China,
This paper proposes a model aims at optimizing area traffic control. We use
network total delay as the objective in the model. In this research, cell
transmission model is used to discretize research time into many intervals and
signal coordination, lane settings, phase, start of green and green split can be
optimized simultaneously. The model is linear in nature and can be solved by
standard branch and bound algorithm.
2 - A Finite Sampling Approach for MPEC
Wenjing Song, Pennsylvania State University, 628B Oakwood
Ave, State College, PA, 16803, United States of America,
, Terry Friesz, Hongcheng Liu, Tao Yao
We study mathematical program with equilibrium constraints (MPEC) from
equilibrium network design problems. We consider the scenario where the
equilibrium constraint has a non-closed-form operator, and propose a finite
sampling approximation with a tunable error bound. Under some regularity
conditions, the approach allows MPEC to be solvable by gradient-based local
schemes to an approximate KKT solution with bounded infeasibility. The
approach is applied to a congestion toll pricing problem.
3 - Travel Time Transmission Model for Network Loading at Merging,
Diverging Segments, and Intersections
Peter J. Jin, Assistant Professor, Rutgers, The State University of
New Jersey, CoRE 613, 96 Frelinghuysen Rd, Piscataway, NJ,
08854, United States of America, ,
Stephen Boyles, Wangsu Hu
The research presents an enhanced travel time transmission model(TTM) based
dynamic network loading (DNL) model for freeway merging, diverging segment
and signalized intersections. The study further evaluates the capability of TTM in
formulating node delay. A network adapted from field flow, signal and network
data of the US-1 at Far West Interchange in Austin TX is used. The modeling
results are compared with the output of a CTM(Cell Transmission Model) based
DNL model.
4 - Vehicle (Lagrangian)-space Freeway Traffic State Estimation:
A Lagrangian Kalman Filter Approach
Han Yang, PhD, Tongji University, Cao’an Road 4800, Shanghai,
China, , Peter J. Jin
Lagrangian coordinates has shown the potential numerical benefits in modeling
mobile sensor data. A new Kalman filter based Lagrangian-space traffic state
estimation model is proposed based on the Travel Time Transmission Model
(TTM). The model is calibrated and evaluated by using a simulation model
calibrated with field data on IH-894 in Milwaukee, Wisconsin and compared with
a CTM-based Kalman filter estimator on space-time coordinates under different
sampling rates of probe vehicles.
5 - Vehicle Routing for the Radiopharmaceutical Industry
Ioannis Akrotirianakis, Siemens Corporate Technology,
750 College Road, Princeton, NJ, United States of America,
, Amit Chakraborty
We develop a model for the distribution of radiopharmaceuticals. Our aim is to
serve many medical imaging centers within a pre-specified time interval at
minimum transportation cost. The model ensures all orders arrive at the centers
before the patients enter the PET scanners. It also takes into consideration the
availability and capacity of the transportation vehicles. The efficiency of the
model is supported by computational results demonstrating substantial savings in
transportation costs.
WC73
73-Room 203B, CC
Data Analytics for Reliability Evaluation and
Maintenance Optimization II
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Eunshin Byon, Assistant Professor, University of Michigan,
1205 Beal Avenue, Ann Arbor, MI, 48109, United States of America,
Co-Chair: Qingyu Yang, Assistant Professor, Wayne State University,
4815 4th street, Room 2167, Detroit, MI, 48202,
United States of America,
1 - Reliability Approximation for k-out-of-n Pairs:
G Balanced Systems
Elsayed Elsayed, Rutgers University, 96 Frelinghuysen Road,
CoRE Building,Room 201, Piscataway, NJ, 08854, United States
of America, , Dingguo Hua
Many applications can be modeled as k-out-of-n pairs: G Balanced systems. When
there are a large number of units in such systems, it is very tedious and
impossible to enumerate the complete set of successful events to obtain the exact
function of its reliability. We provide an approach to obtain approximation for the
reliability of such systems. We validate the approximation by comparing its results
with systems with a small number of units and through the simulation of larger
systems.
2 - A Wiener Process Model for Heterogeneous Degradations
Based on Kriging
Nan Chen, National University of Singapore,
Eunshin Byon
Wiener process plays a crucial role in degradation modeling and condition based
maintenance for critical assets. This paper proposes a Wiener process model based
on Kriging to provide a flexible way to account for heterogeneous degradation
patterns commonly observed due to observable or unobservable factors. It
includes conventional random effects model and covariate model as special cases,
and offers efficient computation. Numerical studies have been conducted to
demonstrate the performance.
3 - Condition-based Joint Maintenance Optimization for a
Large-scale Homogeneous Population
Young Myoung Ko, Assistant Professor, Pohang University of
Science and Technology, 77, Cheongam-ro, Nam-Gu,
Pohang, Gyeongbuk, 790-784, Korea, Republic of,
, Eunshin Byon
We develop a cost-effective maintenance strategy for systems consisting of
homogeneous units. When a large number of units operate in a system,
translating the stochastic degradation processes of individual units into system-
level information remains a significant challenge. We use the asymptotic
distribution for characterizing the system-level condition and analytically derive
the threshold values that trigger maintenance operations. The results are verified
through numerical experiments.
4 - Uncertainty Analysis for Importance Sampling Estimators with
Stochastic Simulations
Youngjun Choe, PhD Candidate, University of Michigan, 1205
Beal Avenue, Ann Arbor, MI, 48109, United States of America,
, Eunshin Byon
Stochastic simulations are widely used to model real-world stochastic systems and
to evaluate the system reliability. Yet, the reliability evaluation can take significant
computational resources as the simulator becomes more realistic. To speed up the
computer experiments, our prior study proposed new importance sampling
estimators. This study establishes the central limit theorems for the estimators and
constructs asymptotically valid confidence intervals.
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