Informs 2015

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INFORMS Philadelphia – 2015
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2 - Optimizing Storage-class Formation in Unit-load Warehouses
Yun Fong Lim, Associate Professor, Singapore Management
University, 50 Stamford Road, #04-01, Singapore, 178899,
Singapore, Marcus Ang
We propose a new approach to optimize storage classes for a unit-load warehouse
with a general layout. Under this approach, the “attractiveness” of each storage
location is determined by its frequency of visits, which is estimated by a linear
program that considers the warehouse’s layout and the products’ arrivals and
demands. We group the locations with similar visit frequencies in the same class.
Our approach gives a lower average travel cost than a cost-based method and a
grid-based method.
3 - Two Single Instruction Multiple Data Implementations for Solving
the Quadratic Assignment Problem
Clara Novoa, Associate Professor, Texas State University, 601
University Dr., San Marcos, TX, 78666, United States of America,
, Apan Qasem, Abhilash Chaparala
We solve the Quadratic Assignment Problem by implementing 2-opt and tabu
search in the Graphical Processing Unit (GPU). For the 2-opt we fine tune the
thread block configuration and exploit inter-thread data locality through shared
memory allocation. In the tabu search we exploit dynamic parallelism. We
experiment with QAPLIB data sets. Tabu search accuracy is very satisfactory while
2-opt performance is impressive. Results are contrasted to a Tabu search GPU
implementation from other authors.
4 - Optimizing Vehicle Travel Speed in Green Vehicle
Routing Problems
Xiaoren Duan, Teaching Assistant, University of Louisville,
Department of Industrial Engineering, University of Louisville,
Louisville, KY, 40292, United States of America,
, Sunderesh Heragu
A Green Vehicle Routing problem with various travel speed is formulated to
minimize total carbon emission. Heuristic algorithm based on Savings Algorithm
and Tabu Search is developed to solve this problem. Numerical experiments show
that the heuristic performs better compared with GAMS and can achieve 15.69%
and 32.27% carbon emission reduction compared with basic G-VRP with and
without time window limitation respectively. Impact of congestion on carbon
emission is also investigated.
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70-Room 202A, CC
RAS Roundtable: Part II Railroad Operations
Efficiency and Recovery
Sponsor: Railway Applications
Sponsored Session
Chair: Erick Wikum, Principal Scientist, Tata Consultancy Services,
1000 Summit Drive, Milford, OH, 45150, United States of America,
1 - Railroad Operations Efficiency and Recovery
Erick Wikum, Principal Scientist, Tata Consultancy Services, 1000
Summit Drive, Milford, OH, 45150, United States of America,
Siddhartha Sengupta, Tao Tang,
Lonny Hurwitz
In the railroad industry, achieving efficient operations and developing the
capability to recover from inevitable disruptions are key to both customer service
and financial performance. In this session, the second of two, panelists from the
railroad industry worldwide explore how to define and measure efficiency and
recovery and share case studies and a vision for the role OR/MS and analytics has
played and can play in operational efficiency and recovery.
SD71
71-Room 202B, CC
Alternative Fuel Vehicles and Sustainable
Transportation I
Sponsor: TSL/Urban Transportation
Sponsored Session
Chair: Changhyun Kwon, Associate Professor, University of South
Florida, 4202 East Fowler Avenue, ENB 118, Tampa, FL, 33620,
United States of America,
1 - A Probabilistic Location Model for Deployment of Electric Vehicle
Charging Stations
Eric Huang, Assistant Professor, Clemson University,
314 Lowry hall, Clemson, SC, 29634, United States of America,
, Shengyin Li
We develop a probabilistic flow-based location model to optimally deploy electric
vehicle (EV) charging stations on traffic network, taking into account the
probability of a demand node becoming an EV adopter. We demonstrate the
model with the Sioux Falls network and solve the model using a Lagrangian
relaxation based algorithm.
2 - Optimization-based Planning of Capacitated Infrastructure for
Intercity Trips of Electric Vehicles
Yu Nie, Northwestern University, ,
Ali Zockaie, Mehrnaz Ghamami
The main purpose of this study is to facilitate the long-distance trips of electric
vehicles. The objective is to minimize the construction cost of charging stations,
battery cost, and refueling delay, while maintaining a certain level of service. To
this end, a nonlinear optimization model is developed. To overcome
computational difficulties of the commercial solvers, a metaheuristic algorithm is
proposed to solve the nonlinear model, as the size of problem grows in real world
case studies.
3 - Multi-period Capacitated Flow Refueling Location Problem
Anpeng Zhang, University at Buffalo, SUNY, 339 Bell Hall,
Buffalo, NY, 14228, United States of America,
, Jee Eun Kang, Changhyun Kwon
We formulate a new flow refueling location problem for electric vehicles,
considering the capacity of rechargers and the time span of construction. The
model will help us determine the optimal locations of recharging stations as well
as the number of recharging modules at each station over multiple time periods.
We develop heuristic methods and present computational experiments based on
the freeway network that spans between Washington DC and Boston.
4 - Infrastructure Planning for Fast Charging Stations in a
Competitive Market
Zhaomiao Guo, University of California, Davis, 614 Sycamore
Lane. Apt. 232, Davis, CA, United States of America,
, Yueyue Fan, Julio Deride
We study the fast charging infrastructure planning under competition using
Multi-agents Optimization Problem with Equilibrium Constraints modeling
framework. We find that the investment pattern could be affected by consumers’
weights on charging price and charging availability: if consumers care more about
charging availability, the investment may cluster to a few locations; on the
contrary, the investment may diffuse through out the network.
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72-Room 203A, CC
IIE Transactions
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Jianjun Shi, Georgia Institute of Technology, 765 Ferst Dr,
Atlanta, United States of America,
1 - Progressive Measurement and Monitoring for Multi-resolution
Data in Surface Manufacturing Considering Cross Correlations
Hui Wang, Assistant Professor, Florida State University, 2525
Pottsdamer St, Tallahassee, FL, 32310, United States of America,
This paper develops a new approach to modeling and monitoring surface
variations by fusing in-plant multi-resolution measurements and process
information. The fusion is achieved by considering cross correlations among
measured data and manufacturing process variables based on cutting dynamics.
The model can make Bayesian inference on surface shapes progressively. A new
monitoring scheme is then proposed for jointly detecting and locating defects
without significantly increasing false alarms.
2 - Prediction of the Failure Interval with Maximum Power Based on
the Remaining Useful Life Distribution
Junbo Son, PhD Candidate, University of Wisconsin-Madison,
1513 University Avenue, Madison, WI, 53706, United States of
America, Qiang Zhou, Shiyu Zhou,
Mutasim Salman
Prognosis of remaining useful life (RUL) of a unit or a system plays an important
role in system reliability. One key aspect of the RUL prognosis is constructing the
best prediction interval. In this paper, we propose a new method, namely
maximum prediction power interval (MPI). The MPI guarantees the best
prediction performance under the given acceptable error range. A numerical
simulation study and case study with real data confirm the better features of MPI
over existing prediction intervals.
SD70