Sunday

SA55
2 - Robust Queueing Theory
Chaithanya Bandi, PhD Student, Massachusetts Institute of
Technology, Cambridge, MA, 02139, United States of America,
cbandi@mit.edu, Dimitris Bertsimas, Nataly Youssef
We propose an alternative approach for studying queueing systems by employing
robust optimization as opposed to stochastic analysis. While traditional queueing
theory relies on Kolmogorov’s axioms of probability and models arrivals and
services as renewal processes, we use the limit laws of probability as the axioms of
our methodology and model the queueing systems primitives by uncertainty sets.
In this framework, we obtain closed form expressions for the waiting times in
multi-server queues with heavy-tailed arrival and service processes. These
expressions are not available under traditional queueing theory for heavy-tailed
processes, while they lead to the same qualitative insights for independent and
identically distributed arrival and service times. We also develop an exact calculus
for analyzing a network of queues with multiple servers based on the following
key principle: a) the departure from a queue, b) the superposition, and c) the
thinning of arrival processes have the same uncertainty set representation as the
original arrival processes. We show that our approach, which we call the Robust
Queueing Network Analyzer (RQNA) a) yields results with error percentages in
single digits (for all experiments we performed) relative to simulation, b)
performs significantly better than the Queueing Network Analyzer (QNA)
proposed in Whitt [1983], and c) is to a large extent insensitive to the number of
servers per queue, the network size, degree of feedback, traffic intensity, and
somewhat sensitive to the degree of diversity of external arrival distributions in
the network.
3 - New Functional Characterizations and Optimal Structural
Results for Assemble-to-order M-Systems
Emre Nadar, Carnegie Mellon University, Tepper School of
Business, Pittsburgh, PA, United States of America,
enadar@andrew.cmu.edu, Alan Scheller-Wolf, Mustafa Akan
We consider an assemble-to-order M-system with multiple components, multiple
products, batch ordering of components, random lead times, and lost sales. We
model the system as an infinite-horizon Markov decision process and seek an
optimal control policy: a control policy specifies when a batch of components
should be produced, and whether an arriving demand for each product should be
satisfied.We introduce new functional characterizations for submodularity and
supermodularity restricted to certain subspaces. These enable us to characterize
optimal inventory replenishment and allocation policies under a mild condition
on component batch sizes via a new type of policy: lattice-dependent base-stock
production and lattice-dependent rationing.
■ SA55
55- Regency Ballroom B - Hyatt
Supply Chain and Operations Issues in
Emerging Economies
Cluster: Operations Research in Emerging Economies
Invited Session
Chair: Mili Mehrotra, University of Minnesota, 321 19th Ave. South,
Minneapolis, MN, United States of America, milim@umn.edu
Co-Chair: Tharanga Rajapakshe, Assistant Professor, University of
Florida, Warrington School of Business Administra, Gainesville, FL,
32606, United States of America,
Tharanga.rajapakshe@warrington.ufl.edu
1 - Improving Agricultural Productivity: Impact of Minimum
Support Prices
Tharanga Rajapakshe, Assistant Professor, University of Florida,
Warrington School of Business Administra, Gainesville, FL, 32606,
United States of America,
Tharanga.rajapakshe@warrington.ufl.edu
A Minimum Support Price (MSP) for an agricultural crop is a guaranteed price,
regardless of volume, at which a governmental entity agrees to purchase the crop
from farmers. This work is an effort to understand the influence of MSP on a
homogenous farming community cultivating a single crop. We analyze the impact
of production cost and yield uncertainty on the MSP. Our results explain the role
of MSP in improving productivity and social utility.
2 - Improving the Milk Supply Chain in Developing Countries:
Analysis under Competing Intermediaries
Liying Mu, University of Texas at Dallas, 800 W. Campbell Road,
Richardson, TX, 75080, United States of America,
muliying@utdallas.edu, Milind Dawande, Vijay Mookerjee
Quality issues in milk – arising primarily from deliberate adulteration by
producers — have been reported in many developing countries. High testing cost
and competition between milk stations (which act as intermediaries by buying
milk from producers and selling it to firms) are identified as the main causes for
the low quality of milk. We propose a mechanism to incentivize high-quality milk
with minimum testing, in the presence of competing milk stations.
INFORMS Phoenix – 2012
74
3 - A Stochastic Optimization Based Decision Support System for
Planning in a Process Industry
Goutam Dutta, Professor, Indian Institute of Management,
Production Quantitive Methods Area, Ahmedabad, Gujarat, Gu,
380015, India, goutam@iimahd.ernet.in, Narain Gupta,
Robert Fourer
We introduce a multi-period, multiple scenario optimization based decision
support system. The DSS is based on a two stage stochastic linear program with
recourse for strategic planning in process industry. The model maximizes expected
contribution subject to material balance, facility capacity, and constraints for nonanticipativity.
We describe how the database structure for the stochastic
optimization model is different in comparison to the multi-period deterministic
model.
4 - Agricultural Cooperatives for Alleviating Poverty:
Models and Analyses
Jaehyung An, PhD Student, UCLA Anderson School of
Management, 11140 Rose Ave. #405, 90034, Los Angeles, CA,
90034, United States of America,
jaehyung.an.2013@anderson.ucla.edu, Soo-Haeng Cho,
Christopher Tang
In emerging markets such as Brazil, China and India, the agricultural sector plays
an important role in their economic growth even though many farmers in these
markets are trapped in the poverty cycle. To break this vicious cycle, government
agencies, Non-Governmental Organizations, and social enterprises are helping
farmers to establish agricultural cooperatives (coops). In this paper, we analyze
the impact of these coops on the production quantities, as well as their impact on
the market.
■ SA56
56- Curtis A- Hyatt
Strategy and Strategic Planning
Contributed Session
Chair: Jay Lee, California State University Sacramento, 6000 J St.,
Tahoe Hall 2079, Sacramento, CA, 95819, United States of America,
jlee@csus.edu
1 - Applying Synthetic Control Methodology in Management
Research: Analyzing the Impact of Government Assistance on
Chrysler’s Performance
Brian Richter, Assistant Professor of Business, Economics, and
Public Policy, Richard Ivey School of Business, University of
Western Ontario, 1151 Richmond Street North, London, ON,
N6A3T9, Canada, brian.richter.@mccombs.utexas.edu, Adam
Fremeth, Guy Holburn
We introduce synthetic control to management research by demonstrating how to
apply the technique to quantify the effects of managerial interventions when a
single observation unit receives a treatment on a single date. The problem with
such scenarios is that counterfactual data is typically lacking. The method,
however, allows us to generate counterfactual data systematically from among
untreated firms. In our application, we assess the impact of government assistance
on Chrysler’s performance.
2 - New Methodology for the Mandatory Minimal Outflow
Representation on Hydrothermal Ssystems Planning
Wellington Conceicão, Instituto Federal de Educacão e Tecnologia,
Campus IFET, Juiz de Fora, Brazil, wellingtcc@yahoo.com.br,
André Marcato, Joao Passos Filho, Rafael Brandi, Tales Ramos,
Ivo Silva Junior
This work presents an alternative methodology to supply the mandatory
minimum outflow in the problem of long-term operation planning of
hydrothermal interconnected systems. It was used the strategy of trying to
maintain the storage reservoirs within safe levels through a minimum curve of
stored energy level. The simulation results shows that when using this
formulation a decrease in the minimum outflow deficit is obtained.
3 - Game Theoretic Analysis of Inter-firm R&D Expenditure
Distribution in Semiconductor Manufacturing
Tom Heaps-Nelson, PhD Candidate, Massachusetts Institute of
Technology, 77 Massachusetts Ave., Cambridge, MA, 02139-4301,
United States of America, heaps@mit.edu
Soaring R&D and capital investment costs has left the semiconductor industry
with a handful of large chipmakers and semiconductor equipment suppliers. This
paper employs non-cooperative game theory along with detailed empirical
estimates of firm payoffs as a framework for understanding how the largest
industry players are likely to share the burden of R&D funding for two ongoing
industry-wide process technology transitions: EUV lithography and 450mm
wafers.