Informs 2015

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INFORMS Philadelphia – 2015
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4 - The Effect of Supply Base on Ordering Behavior
Haresh Gurnani, Professor, Wake Forest University, School of
Business and Center for Retail, Winston Salem, NC, 27106,
United States of America,
Karthik Ramachandran, Saibal Ray, Yusen Xia
Previous experimental research in newsvendor problem has mostly focused on
ordering from a single supplier; we investigate how the availability of multiple
suppliers would influence the order size and allocation decisions with uncertain
demand. We also study how the supply base impacts behavioral insights
developed under a single supplier setting.
MD54
54-Room 108A, CC
Computational Optimization and Statistical Methods
for Big Data Analytics: Applications in Neuroimaging
Cluster: Tutorials
Invited Session
Chair: W. Art Chaovalitwongse, University of Washington - Seattle
3900 Northeast Stevens Way, Mechanical Engineering Building,
Room G6, Seattle, WA, United States of America,
1 - Computational Optimization and Statistical Methods for Big Data
Analytics: Applications in Neuroimaging
W. Art Chaovalitwongse, University of Washington - Seattle,
3900 Northeast Stevens Way, Mechanical Engineering Building,
Seattle, WA, United States of America,
Shuai Huang
This tutorial describes recent advances in computational optimization and
statistical methodologies in the emerging research area of Big Data analytics, with
a focus on classification, regression and feature selection. We discuss the
mathematical and statistical modeling of these problems and provide an
application to brain imaging. Analytics of neuroimaging data can provide a
unique and often complementary characterization of the underlying
neurophysiological process that may be useful in clinical diagnosis of brain
diseases.
MD55
55-Room 108B, CC
Environmental Application and Computational
Aspects of Efficiency and Productivity Analysis
Cluster: Data Envelopment Analysis
Invited Session
Chair: Herbert Lewis, Associate Professor, Stony Brook University,
College of Business, Stony Brook, NY, 11794-3775,
United States of America,
1 - An Algebraic Modeling Language Package for Solving
Large-Scale Data Envelopment Analysis Problems
Wen-Chih Chen, National Chiao Tung University, 1001 Ta Hsueh
Rd., Hsinchu, Taiwan - ROC, ,
Yueh-shan Chung
Algebraic modeling languages for mathematical programming provide a flexible
and powerful tool for DEA computation. This talk introduces an algebraic
modeling language package for solving large-scale DEA problems. While taking
the advantage of flexibility and powerful solvers from a modeling language, this
package can solve larger-scale DEA problems without limitation on constraints
and variables, and with better computational performance.
2 - Frontier Estimation via Penalized Concave Regression
Abolfazl Keshvari, Dr., Aalto University School of Business,
Runeberginkatu 22-24, Helsinki, 00100, Finland,
Concave regression is an important tool in estimating a productive efficiency
frontier. However, computing this estimator is a very difficult and time consuming
task. The computational burden rises very quickly with increasing numbers of
observations. We develop an unconstrained quadratic programming (QP) problem
to the (monotonic) concave regression, which outperforms the conventional
constrained QP problem. Using our approach, we solve a problem with hundreds
of observations in some seconds.
3 - Productivity Growth and Environmental Efficiency:
A Global Malmquist-luenberger Index Analysis
Jayanath Ananda, Dr, Central Queensland University,
120 Spencer Street, Melbourne, 3000, Australia,
, Benjamin Hampf
The paper analyses the productivity of the urban water sector using the global
Malmquist-Luenberger index while incorporating an undesirable output –
greenhouse gas emissions. Findings indicate that the productivity growth of the
sector has declined in cumulative terms. The water source, the level of
wastewater treatment and production density showed a statistically significant
influence on the relative efficiency of urban water utilities.
4 - Saving Water in California: using DEA to Allocate
Usage Reductions
Herbert Lewis, Associate Professor, Stony Brook University,
College of Business, Stony Brook, NY, 11794-3775, United States
of America, , Diana Hagedorn,
Thomas Sexton
Governor Brown of California has directed the State Water Resources Control
Board to implement mandatory urban water reductions of 25%. In this paper, we
use DEA in 5 of the 9 water use categories, comprising 95% of the state’s water
usage, to identify the reductions possible in each county through the elimination
of inefficiency. Where the elimination of inefficiency is insufficient to meet the
goal, we use a second linear programming model to allocate additional cuts in an
equitable manner.
MD56
56-Room 109A, CC
Spatial Analysis
Sponsor: Location Analysis
Sponsored Session
Chair: Alan Murray, Professor, Drexel University, 3141 Chestnut Street,
Philadelphia, PA, 19104, United States of America,
1 - Transmax 2: An Expanded Transit Route Covering Model
Richard Church, Professor, University of California, Santa
Barbara, Santa Barbara, CA, 93106, United States of America,
, Timothy Niblett
Current et al. (1984, 1985) were the first to suggest a routing problem to
minimize distance and maximize demand coverage. This problem characterizes
the principal goals of transit route design. Since that time there have been a
number of formulations, involving elements like route extension and multiple
route design. We discusses the TRANSMax model of Curtin and Biba (2011) and
based upon that propose a new, expanded formulation called TRANSMax 2,
which allows for greater flexibility.
2 - Max Flow with Buyout: Identifying The Minimum Number of
Facilities/personnel Required to Meet Demand
Blair Sweigart, Operations Research Analyst, US Coast Guard,
216 Maryland Ave, Norfolk, VA, 23504, United States of America,
The USCG needs to ID deployability of personnel and mission impact. This
reduces to a facility location problem: mission demands are demand nodes,
personnel are potential facilities, arc capacities are time requirements. Objectives
are: determine min. number of personnel needed to meet demand; ID if a person
is critical. If not, ID the tradeoff cost. This is a modification of a max-flow
algorithm that pushes excess out of the network intelligently to minimize the
required number of personnel.
MD56