Informs 2015

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INFORMS Philadelphia – 2015
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2 - Generalized Sequential Assignment Problem
Arash Khatibi, University of Illinois, 201 North Goodwin Avenue,
Urbana, IL, United States of America, ,
Sheldon Jacobson
The Sequential Stochastic Assignment Problem (SSAP) assigns sequentially
arriving tasks with stochastic parameters to workers with fixed success rates so as
to maximize the total expected reward. This paper uses the Secretary Problem to
propose assignment policies for the SSAP, when there is no prior information on
task values. This paper also discusses the Doubly Stochastic Sequential
Assignment Problem (DSSAP), which is an extension of SSAP with the workers’
success rates assumed to be random.
3 - Stochastic Versus Dynamic Programming for a Transportation
Procurement Problem
Francesca Maggioni, Assistant Professor, University of Bergamo,
Via dei Caniana n 2, Bergamo, 24127, Italy,
, Luca Bertazzi
We consider the problem of a producer which has to ship a load to a customer at
discrete times and fixed horizon. Different companies offer a transportation price
with realization available at the end of the time period. A penalty is paid for the
quantity that remains to be sent. We consider two variants of the problem: the
minimum expected cost and the min-max total cost. We compare the
performance of stochastic and dynamic programming approaches. Theoretical
worst-case results are provided.
4 - Data-driven Schemes for Resolving Misspecified MDPs:
Asymptotics and Error Analysis
Hao Jiang, UIUC, 104 S. Mathews Ave., Urbana, IL,
United States of America, Uday Shanbhag
We consider the solution of a finite-state infinite horizon Markov Decision Process
(MDP) in which both the transition matrix and the cost function are misspecified,
the latter in a parametric sense. Learning-enhanced value and policy iteration
schemes are proposed and shown to be convergent almost surely. Finally, we
present a constant steplength misspecified Q-learning scheme and provide an
error analysis.
SD15
15-Franklin 5, Marriott
Nonlinear Optimization Algorithms
Sponsor: Optimization/Nonlinear Programming
Sponsored Session
Chair: Frank E. Curtis, Lehigh University, 200 W Packer Ave,
Bethlehem, PA, 18015, United States of America,
1 - A Stochastic Programming Model for Nurse Staffing in Post-
Anesthesia Recovery Units
Yueling Loh, Johns Hopkins University, 3400 North Charles
Street, Baltimore, MD, 21218, United States of America,
, Sauleh Siddiqui, Daniel Robinson
We present a stochastic programming model to determine nurse staffing
requirements in Post-Anesthesia Recovery Units under high variability in patient
flow and length-of-stay. We will formulate the problem as a two-stage stochastic
mixed integer program and provide some numerical results.
2 - Distributed Parallel Coordinate Descent Methods for Sparse
Inverse Covariance Problem
Seyedalireza Yektamaram, Lehigh University, Mohler Laboratory,
200 West Packer Ave, Bethlehem, PA, 18015, United States of
America, Katya Scheinberg
In graphical models, recovering the structure of underlying graph corresponding
to conditional dependencies of random variables is of great importance,which is
obtained by recovering the corresponding Sparse Inverse Covariance matrix.
However, as the problem size grows larger, efficiency of most solution approaches
reduce significantly, thus using distributed parallel techniques becomes essential.
Here we explore distributed parallel coordinate descent methods to solve this
problem efficiently.
3 - A Nonconvex Nonsmooth Optimization Algorithm with Global
Convergence Guarantees
Frank E. Curtis, Lehigh University, 200 W Packer Ave,
Bethlehem, PA, 18015, United States of America,
, Xiaocun Que
An algorithm for minimizing nonconvex nonsmooth objective functions is
presented. The algorithm is based on a BFGS strategy, enhanced with gradient
sampling mechanisms to ensure convergence to a stationary point with
probability one. An open source C++ implementation of the algorithm is also
described along with results for a set of test problems.
SD16
16-Franklin 6, Marriott
New Optimization Modeling and Effective Techniques
Sponsor: Optimization/Linear and Conic Optimization
Sponsored Session
Chair: Jiming Peng, Associate Professor, University of Houston, UH,
Dept of Industrial engineering, Engineering Bldg 2 221A., Houston, TX,
77204, United States of America,
1 - Dropconnect in Deep Learning via Lagrangian
Diego Klabjan, Professor,
Mark Harmon
Dropconnect is a regularization technique for deep neural nets when random
weights are enforced to be zero. We present a Lagrangian-based algorithm for
restricting weight values.
2 - New Global Algorithm for Linearly Constrained
Quadratic Programming
Jiming Peng, Associate Professor, University of Houston, UH, Dept
of Industrial engineering, Engineering Bldg 2 221A., Houston, TX,
77204, United States of America,
How to find the global optimal solution to LCQP has been a long-standing
challenge in optimization. In this talk, we introduce a new design framework for
LCQP that integrates several simple effective optimization techniques such as
Lagrangian methods, Alternate update method, convex relaxation, initialization
and partitioning. We establish the global convergence of the algorithm and
estimate its complexity. Promising numerical results for large-scale LCQPs will be
reported.
3 - An Optimization Perspective on Systemic Risk
Aein Khabazian, PhD Student/research Assistant, Univirsity of
Houston, UH, Dept of Industrial engineering, Engineering Bldg 2,
Houston, TX, 77204, United States of America,
, Jiming Peng, Aida Khayatian
We consider the issue of assessing the systemic risk under uncertainty in a
financial system based on the model proposed by Eisenberg and Noe, in which
the interbank liabilities are assumed to be known, and the non-interbank assets
are assumed to be constant. However, in real world application this information is
typically unknown or subject to market fluctuation. In this regard, we develop
robust optimization and worst case optimization to account for the uncertainties
in the constraints.
SD17
17-Franklin 7, Marriott
Transportation Network Modeling and Optimization
Sponsor: Optimization/Network Optimization
Sponsored Session
Chair: Vladimir Stozhkov, University of Florida, 2330 SW Williston Rd
Apt 2826, Gainesville, FL, 32608, United States of America,
1 - A Distributed Hierarchal Shortest Path Algorithm for Large-Scale
Transportation Networks
Ala Alnawaiseh, Postdoctoral Researcher, Southern Methodist
University, 3101 Dyer St.,, #219, Dallas, TX, 75205,
United States of America,
Khaled Abdelghany, Hossein Hashemi
This paper presents a distributed hierarchical shortest path algorithm for large-
scale transportation networks. The algorithm integrates a network augmentation
as well as a divide-and-conquer techniques to solve the all-to-all shortest path
problem in a distributed fashion. Preliminary results that illustrate the superiority
of the algorithm are presented.
2 - How to Design an Effective Off-hour Delivery (OHD) Program:
A Network Design Perspective
Sevgi Erdogan, Faculty Research Associate, University of
Maryland-NCSG, 1112 J Preinkert Field House, College Park, MD,
20742, United States of America,
Jiangtao Liu, Wenbo Fan, Xuesong Zhou
The OHD has been considered as an effective policy to overcome the negative
externalities like congestion due to freight traffic in urban areas during business
hours. This study proposes a network design approach to determine links to be
restricted for truck traffic to shift truck demand to off-hours and routes to less
congested areas so that the maximum benefit from an OHD program can be
achieved. The approach will guide cities in implementing effective OHD programs.
SD15