2002-2003 Program: Optimization
Talk
Abstracts:
IMA
Tutorial:
September
9-13, 2002
Material
from Talks
Daniel
Bienstock (Department of Industrial Engineering and
Operations Research, Columbia University) dano@ieor.columbia.edu
http://www.ieor.columbia.edu/~dano
Large-Scale
Linear Programming Presentation
Slides
Linear
Programming is the central tool of Mathematical Programming.
Linear programming models are flexible enough to adequately
describe many realistic problems arising in modern industrial
settings, while at the same time taking advantage of the considerable
expertise on computational linear algebra that has been developed
during the last fifty years. As a result, linear programming
models are abundantly used in logistics, transportation, finance
and many other practical applications.
Linear
Programming has undergone profound changes during the last twenty
years, resulting in codes that are thousands (and sometimes,
millions) of times faster than what was available just fifteen
years ago. Yet difficult challenges persist, in the form of
large-scale linear programming problems arising in routing,
network design, chip design and other settings. In fact, large
problem instances render even the best of codes nearly unusable.
In
this lecture we will survey fundamentals of Linear Programming
theory, with special emphasis on recent developments, and in
particular, on techniques geared to handling very large instances.
E.
Andrew Boyd
(Senior Vice President, Science and Research and Chief Scientist,
PROS Revenue Management) aboyd@prosrm.com
http://www.prosrm.com
Revenue
Management and Dynamic Pricing Slides: html
pdf
Revenue
management has been employed with tremendous success in the
airline industry to manage ticket prices. Hailed by Bob Austrian
of Banc of America Securities as ?one of the most exciting inevitibilities
ahead? whose implications for profitability ? cannot be stressed
enough,? many industries have adopted or are in the process
of adopting revenue management and dynamic pricing.| This tutorial
presentation will provide an introduction to the practice of
revenue management and dynamic pricing while introducing the
mathematical concepts that drive the underlying value proposition.
Modeling and computational challenges will highlighted.
John
R. Birge
(Dean McCormick School of Engineering and Applied Science, Northwestern
University) jrbirge@northwestern.edu
Stochastic
Optimization Slides:
pdf
Supply
chain and logistics problems inevitably involve the consideration
of random quantities such as uncertain demands, supplies, travel
times,costs, and prices. This tutorial will present some fundamental
stochastic optimization models to include such random events.
The models will include decisions for network design, capacity,
vehicle flows, and contract terms. We will then present a general
framework for these stochastic optimization problems, their
basic properties, algorithms, and solutions. The discussion
will focus on discrete-time models but will also introduce continuous-time
models and real-option approaches to deal with financial issues
in supply chains and logistics.
Robert
Fourer
(Northwestern University) 4er@iems.nwu.edu
AMPL
Hands-on Session Slides:
html
pdf
ppt
Modeling
languages describe optimization problems to computer systems
in the symbolic terms familiar to people, rather than in the
obscure input forms convenient to optimizing algorithms. In
typical use, a symbolic model and specific data are automatically
translated to a problem instance and submitted a solver; subsequent
results are automatically retrieved and translated back to forms
convenient for inspection and analysis. A single modeling language
can be interfaced to many solvers, providing access to techniques
for a range of linear, nonlinear, and discrete problems, and
encouraging comparisons between alternative algorithms for individual
problem types.
This
session will provide an introduction to AMPL, one of the more
widely used optimization modeling languages, through a series
of simple linear and integer programming models. Participants
will have a chance to experiment with AMPL models and to ask
how a modeling language might be applied to problems of special
interest to them.
Gregory
Glockner,
Ph.D. (Optimization Product Manager, ILOG, Inc., Mountain View,
CA) gglockner@ilog.com
Discovering
Combinatorial Optimization with the ILOG Optimization Suite
Slides:
html
pdf
powerpoint
ILOG is the world leader in optimization technology, supplying
the world's most powerful and comprehensive components for developing
optimization applications. With core algorithms from mathematical
programming and constraint programming, the ILOG Optimization
Suite is highly effective at solving industrial and research
problems in constraint satisfaction and optimization. The ILOG
Optimization Suite powers the leading software for supply chain
management and logistics optimization.
In
this tutorial, we will use a rapid development system to explore
basic combinatorial optimization problems. The sample problems
will illustrate powerful techniques like constraint propagation
and iterative optimization, which are invaluable to solving
real-world optimization applications in supply chain and logistics.
The tutorial will emphasize hands-on examples and their relationship
to larger applications.
Jon
Lee
(IBM T.J. Watson Research Center) jonlee@us.ibm.com
Service
Parts Logistics
Large
spare-parts inventory and distribution logistics operations
involve over 10K part numbers, with annual volume in the several
M. In recent years, market forces have changed the way these
services are sold and delivered. These changes are being made
to address the needs to improve customer satisfaction and to
drive down costs associated with service delivery. For example,
the IBM/ITS North American maintenance organization focused
on specific areas in order for IBM to drive down costs and improve
the customer experience. Specifically, (i) a value-based pricing
scheme tied to service levels, and (ii) a call screening process
enabling remote diagnosis of problems. These changes led to
challenging logistics requirements involving the deployment
of labor and parts in order to satisfy contracted service levels
while containing inventory and transportation costs.
The
Optimization Center of the Mathematical Sciences Department
at IBM Research and IBM's ITS/SPS organization, in collaboration
with the Lehigh University Department of Industrial and Manufacturing
Engineering have been developing a next generation logistics
system designed for flexible and optimal control of spare parts
inventories. IBM has migrated to a Parts Procurement Time (PPT)
performance measure, which monitors whether the frequency at
which parts are delivered to a customer location within a contractually
determined time interval aggregated across machine service groups
and geographies meet acceptable thresholds. I will describe
the optimization model and algorithms that are currently being
successfully deployed which have led to increased service, and
reduced transportation costs, and dramatically reduced inventory
costs.
Andrew
J. Miller amiller@engr.wisc.edu
(University of Wisconsin)
Integer
Programming Slides: t410g1us.htm
t410g1us.pdf
hyg2gny9.htm
hyg2gny9.pdf
IP_IMA.ppt
IP_IMA_2.ppt
This tutorial is about the theory and algorithms for solving
mixed-integer programming problems. We will focus on the recent
LP-based methodologies of branch-and-cut and branch-and-price,
which are the techniques that make it possible to solve large-scale
problems, but we will discuss briefly other heuristic methods
as well.
Martin
W.P. Savelsbergh
(School of Industrial and Systems Engineering, Georgia Institute
of Technology) mwps@isye.gatech.edu
Vehicle
Routing & Scheduling Slides:
VRP_part1.pdf
VRP_part2.pdf
This
tutorial is about models and algorithms for solving vehicle
routing and scheduling problems. We will cover heuristic as
well as optimization techniques. We will demonstrate the use
of these techniques in two practical applications: vendor managed
inventory resupply and linehaul scheduling.
Martin
Skutella
(Current Affiliation: Sloan School of Management, Room E40-123,
Massachusetts Institute of Technology, 50 Memorial Drive, Cambridge,
MA 02142-1347, skutella@mit.edu
Permanent address in Berlin: Technische Univ. Berlin,
Fak. II - Mathematik und Naturwissenschaften, Institut f. Mathematik,
Sekr. MA 6-1, Str. des 17. Juni 136, 10623 Berlin, Germany,
phone: +49+30-314-25747 fax: +49+30-314-25191 skutella@math.tu-berlin.de)
Flows
Over Time Slides:
pdf
The
intention of the tutorial is to give an introduction into the
area of "flows over time" or "dynamic flows." Flows over time
have been introduced about forty years ago by Ford and Fulkerson
and have many real-world applications such as, for example,
traffic control, evacuation plans, production systems, communication
networks, and financial flows. Flows over time are modelled
in networks with capacities and transit times on the arcs. The
transit time of an arc specifies the amount of time it takes
for flow to travel from the tail to the head of that arc. In
contrast to the classical case of static flows, a flow over
time specifies a flow rate entering an arc for each point in
time and the capacity of an arc limits the rate of flow into
the arc at each point in time. The topics covered range from
the classical results of Ford and Fulkerson on maximal s-t-flows
over time to very recent approximation results for flows over
time with multiple commodities and costs.
Material
from Talks
IMA
Tutorial: Supply Chain and Logistics Optimization September 9-13,
2002
2002-2003
Program: Optimization
