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© Copyright IBM Corporation 2005
Opportunities in Operations Research
Brenda DietrichApril 8, 2006
2 © Copyright IBM Corporation 2005
Agenda
Personal Historical PerspectiveTechnology Trends OR Opportunities: Internal ViewOR Opportunities: External ViewInterconnections to other IT trendsPriorities Going Forward
3 © Copyright IBM Corporation 2005
Personal Historical Perspective
1980’s – Manufacturing Logistics-OR models for portions of IBM manufacturing lines
Focus on layout of line
- “micro logistics” – models of individual automated machines-Scheduling of process or “sector”
1990’s – Optimization Center-Enterprise level planning -Supply chain models and tools-OSL-Client OR applications-First applications in IBM services
2000’s – Mathematical Sciences- “Analytic” applications throughout IBM-Client engagements with IBM Consultants-Open Source Software
4 © Copyright IBM Corporation 2005
Trends
Framework for thinking about trends
Technology Trends-Processing Speed -Data Availability-Communication-Software and Application Architecture
Business Trends
The Event Driven World
5 © Copyright IBM Corporation 2005
The Technology Curve of Change
•The first technological steps--sharp edges, fire, the wheel--took tens of thousands of years.
•By 1000 A.D. a paradigm shift required only a century or two.
•In the nineteenth century, there was more technological change than in the nine centuries preceding it
•In the first twenty years of the twentieth century, we saw more advancement than in all of the nineteenth century.
•Today paradigm shifts occur in only a few years time. The World Wide Web did not exist in anything like its present form just a few years ago; it didn't exist at all a decade ago.
Source: The Law of Accelerating Returns –Ray Kurtzweilhttp://www.kurzweilai.net/articles/art0134.html?printable=1
6 © Copyright IBM Corporation 2005
The Curve of Change
World Population
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2
4
6
8
10
12
1500
1550
1600
1650
1700
1750
1800
1850
1900
1950
2000
2050
Bill
ions
Low Medium High
Source: Population Division of the Department of Economic and Social Affairs of the United Nations http://esa.un.org/unpp
Source: The Gary Hilbert Letterhttp://www.thegaryhalbertletter.com/newsletters/population.htm
1. There are more people alive today... than all the humans who have ever lived since the dawn of civilization.
2. 99% of all the scientists who have ever lived... are alive today.
3. More info is contained in one daily edition of the New York Times… than wasavailable inthe entire 17th century.
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Technology Deployment Pace Accelerates (US data)
0
10
20
30
40
50
60
70
80
90
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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Hou
shol
ds (%
)
WWW
Credit Card
A
Business & Technology Infrastructure
Customer Segments
Insi
ght
Ris
k &
Fin
anci
alM
anag
emen
t
Processing
Distribution
Manufacturing
IBM Research
© 2005 IBM Corporation 8
Processing Speed: What $1000 Buys
1900 1920 1940 1960 1980 2000 20201E-6
1E-3
1E+0
1E+3
1E+6
1E+9
1E+12MechanicalElectro-mechanical
Vacuum tubeDiscrete transistor
Integrated circuit
Year
1,000,000,000,000
1,000,000,000
1,000,000
1,000
1
0.001
0.000001
Com
puta
tions
/ se
c
after Kurzweil, 1999 & Moravec, 1998
9 © Copyright IBM Corporation 2005
Data and Storage Trends
Storage Density
0.001
0.01
0.1
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10
100
1000
1980 1990 2000 2010
Are
al D
ensi
ty (G
b/in
2)
Drives CD Blue LaserTape Lab Demos Millipede
Storage Price
0.0001
0.001
0.01
0.1
1
10
100
1000
1980 1990 2000 2010
Pric
e / M
egaB
yte
($)
HDD DRAM Flash Paper/Film
Price decreasing rapidly, now significantly cheaper than paper.
Progress could slow down due to technological challenges
Progress from breakthroughs, including MR, GMR heads, AFC media
Range of Paper/Film
Since 1997 raw storage prices have been declining at 50%-60% per year
1" Micro drive
2.5" HDD
3.5" HDD
Flash
DRAM
CD-ROM
DVD-R
i
10 © Copyright IBM Corporation 2005
Data Volume is Exploding
Machine Generated Data-Sensors/Biometrics/Voice-High Volume-Not amenable to
traditional database architectures
Authored Data-Created by hand-Historically in database-Low volume (but high
value)
Machine-generated versus authored data
.001
.01
.1
1
10
100
1,000
1995 2000 2005 2010 2015
Storage online
In databases
All medical imaging
Personal multimedia
Surveillance data
Text data
Static Web data
Gig
a-by
tes
/ US
cap
ita /
year
Machine generated data is increasing at an exponential pace which causesusability concerns
11 © Copyright IBM Corporation 2005
Data Being Captured at Increasing Spatiotemporal Resolution
People sensors:location (inferred from that of their devices), activities (inferred from calendar and desktop information), biometrics, etc.
Place sensors:room status (inferred from anonymous motion/sound detectors), presence (of people and things), congestion (inferred from pressure pads or camera images), etc.
Thing sensors:location (RFID), status (monitoring sensors such as telematics, desktop), etc.
Business sensors:context from databases (medical data, credit history, location history), context from processes
12 © Copyright IBM Corporation 2005
Connectivity: Internet Access Trends
Note: Subscriptions - not users !
Global Internet Subscriptions
0
200
400
600
800
1000
2001 2002 2003 2004 2005 2006 2007
Mill
ions
Asia/Pacific North AmericaWestern Europe Middle East & AfricaLatin America Eastern Europe
Source: Gartner Dataquest (June 2003)
Broadband Connectivity
Source: IDC, May 2003
US29% AP
39%
W.Eur25%
row7%
Worldwide Broadband Connections Trend
0
100
200
300
2003 2004 2005 2006 2007
Mill
ions
► Growth driven by rapid decline in price of service ► Largest growth in Asia Pacific ► DSL/cable modems dominate “Last Mile”
2003 Broadband Connectivity by Geography
Wireless
0
50
100
150
2002 2003 2004 2005 2006 2007
(Units in Thousands)
Source: In-Stat/MDR April 2003
Worldwide Hotspot Locations► 150,000 public WLAN
hotspots by 2007► 35M WLAN access units
by 2005
13 © Copyright IBM Corporation 2005
Communication trends
-Trend Doubling Period
-Communications- bits/dollar before 1995 79 months-Communications- bits/dollar with DWDM 12 months
-Maximum Internet Trunk Speed in service 22 months
- Internet Traffic Growth 1969-1982 21 months - Internet Traffic Growth 1983-1997 9 months - Internet Traffic Growth 1997-2008 6 months
- Internet Router/Switch Max Speed until 1997 22 months - Internet Router/Switch Max Speed after 1997 6 months
http://www.packet.cc/files/InternetTrends.htm
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Web 2.0 Meme (Web Culture)
The Web as
“The Platform”
Tools: RSS, AJAX, PHP,
Ruby
Services, not packaged software
Architectural participation
Small pieces loosely joined, or
“re-mixed”
Harnessing collective
intelligence
Software that gets better as more people use it
Standards: REST, XHTML
Techniques: Mash-up, wiki,
tagging, blogging
Rich user experiences
Light-weight programming
models
Zeitgeist of the current internet generation
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Making Web Services as Simple as Spreadsheets
1. Locate a web service providing stock quotes in dollars
2. Reuse an existing web service for currency exchange
3. Goal: new aggregate web service, Stock Quote in any currency
Radically simplified creation, publishing, deployment, and reuse of web services
Result: Integration by end-users in minutes
click to run
4. Solution: Wire A and B together, using a spreadsheet metaphor, to create and publish a new web service
16 © Copyright IBM Corporation 2005
The Event-Driven World
There is a growing need to monitor, capture, process, and store massive volumes of time-dependent events- Smart sensors, RFID, program trading, fraud management, risk and compliance, intelligent oil
field, location based service, logistics, presence (SIP), in-line analytics, etc.
An increasing number of companies are addressing the needs of the time-dependent infrastructure market- Developing event engines that route, transform and derive events from multiple streams- Delivering content management solutions supporting large volumes of time-dependent data- Extracting event information from applications (ERP, CRM, etc.) and sensors
Standards are emerging- Real-time Specification for Java (JSR 1), Distributed Real-Time Specification (JSR 50)- Web Services Notification and Web Services Eventing- OMG Data Distribution Service (DDS) and IEEE 1516 for battlefield simulation
Middleware will evolve to deal with the throughput and time-dependent needs of the event-driven world. New programming models and tools will also emerge- Data integration will evolve to event integration
The accelerating need to handle large volumes of time-dependent events will give rise to new classes of middleware, programming models, and tools
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The Prerequisites for effective use of Operations Research are in place or being put in place
Computational AdvancesExponential increases in
processing power
Advanced Algorithmsand New Models
OptimizationSimulation
Data mining
Data AvailabilityData is abundant, including
real-time data
Success of OR deployments will depend upon fit, value, and ease of use
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$10M savings 30% productivity from tools that teach
Ford’s Auto Plant SimulationHR Management
$890M reduction in finished goods inventory; improved on time shipments 63% to 92%
John Deere’s Inventory and Asset Management Supply Chain
$200M savings; optimal forecasting and allocation
U.S. Army RecruitingHR Management
$200M cost savings from optimal sourcingMotorola’s Supplier Negotiation ProcessesProcurement
$750M savings in inventoryIBM’s Asset ManagementSupply Chain
$80M revenue growth new offerings increase customers choices
Merrill Lynch ‘s Pricing Analysis ProcessesFinancial Services
$150M per year cost savingsBritish Telephone’s Field Engineer SchedulingWorkforce Scheduling
Reduced staff 10%; increased utilization 20% maintained SLA>90%
Bombardier’s Aircraft Operations and Crew Scheduling Processes
Workforce Scheduling
MeasuresCompanyProcess
Examples of Savings from Operations ResearchEdelman Prize
Institute for Operations Research and Management Sciences (INFORMS)
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Examples of Savings from Operations Research (continued)
$44M savings; reduced number of trucks, improved customer service levels
Waste Management’s Route Optimization
Transportation
$5M savings from optimal portfolio of more than 150 negotiated contracts
Texas Children’s Hospital’s Contract Negotiations
Healthcare
$30M direct savings, 50% more capacity, $300M cost avoidance to build more
Hong Kong Container Terminal Container Movements
Transportation
$170M savings optimal schedules reduced number of trains, shipment times
Canadian Pacific Railway’s Train Schedules
Transportation
Moved from #6 to #2 in its market by optimal mailings
Mail Order Firm’s Data-driven MarketingMarketing and Advertising
$50M revenue per year from optimal capacity (ad air-time) to meet customer demand
NBC’s Advertising SalesMarketing and Advertising
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OR Opportunities: Internal view
Hybrid approaches, including techniques from AI as well as OR-AI based search strategies with MP based fathoming-Mathematical Optimization implementations of support vector machines
Non linear Optimization with some discrete variables
Effective exploitation of massive parallelism-1000’s of nodes
Robust Optimization
OR applications as a web service Open Source www.coin-or.org
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Hybrid Approaches
Hybridization of traditional OR methods with CP (mainly) and other AI formalisms-CP based search strategies in B&B-LP based fathoming in CP search- “all different” constraint -GRASP for solution completion at B&B nodes-Highly successful new conference devoted to this: CP-AI-OR (workshop since 2000,
full int'l conference since 2004)-A large percentage of papers dealing with hybridized CP/LP models and algorithms
in CP'05 (a significant jump compared to CP'04).
Extensions to other “search” approaches?-How to use LP/IP methods be used within a neighborhood search framework to
identify attractive directions?-How to use LP/IP within a population based method to identify attractive
recombination or mutation opportunities?Or will intelligent recombination defeat the benefits of randomization?
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Optimization methods in Data Mining and Machine Learning
Math programming formulations of the separation problemActive set approach to dealing with large data setsProvable algorithm performance
-Workshop on "Machine Learning, SVM and Large Scale Optimization", Turnau, Germany 2005.
-Workshop on ”Mathematical Programming in Data Mining and Machine Learning,”McMaster University 2005,
-EURO “Summer Institute on Optimization in Data Mining” was held in Ankara in July of 2004.
- “Workshop on Mathematical Programming in Data Mining and Machine Learning” will be held in January 2007
23 © Copyright IBM Corporation 2005
Non-linear Discrete Optimization
Mixed Integer Non Linear Programming (MINLP)- Solver development: some work underway in COIN-OR - Formulation issues: expect that as with LP/IP early solvers will be sensitive to problem formulation
Applications: Process industry, Finance Industry- Business goal is finding feasible, good solutions quickly.- Dynamic environment, imprecise data, approximate formulation limit value of optimizing
Need new algorithmic paradigm aimed at quickly finding good feasible solutions
Experimentation- Problem testbed that allows for experimentation with formulation and noise in the data- Diversity in domain, structure, and size.-
Fundamental structural results and complexity issues - Identification of special sub-problems
Challenges: - Finding faster methods for SDP (and in particular second-order cone programming) to take advantage of the tight
bounds in MINLP
Formulations that lead to good feasibility heuristics
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Massive Parallelization
High performance machines like BlueGene have 1000’s of compute nodes-2 chips, each with Dual 700mHz processors with 4MB L3 Cache, -Up to 1GB memory
Open source SW allows a solver on every compute nodeWhich classes of problems are most suited to this environment?-Clearly the “embarassingly” parallel, but what else?
Which classes of solution algorithms are most suited to this environment?-With Branch and Bound philosophy, problem list grows slowly-Tradeoff between computation and inter-node communication
How can/should large problems be partitioned?-Reformulations that facilitate partitioning
What new problem spaces can be addressed with such massive computing power?
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Robust Optimization
Community is still grappling with defining the right modeling paradigm.
One class of robust Optimization deals with resource planning with an infinite or rolling horizon.- Have nearly complete data for the first few periods, - At best partial data for the later periods.
Can forecast “average” data for later periods (e.g;, number of rides, average duration) And have historical data that could be sampled
Typical OR approach- Fix the planning horizon “far enough”- Use actual data for first few periods, estimates for later periods- Resolve at least at the end of each period
Issues: - Is the first period solution close to the true stochastic infinite-horizon solution- How much does the quality of the estimate for later periods matter?
Can we borrow ideas from statistical physics and "probabilistic reconstruction“- Unknown future is providing some sort of randomized boundary condition. - In some cases the local (now) solution is essentially independent of the boundary condition. - Expect a phase transition in some parameter such as the fraction of demand that arises unpredictably, so that
tightly coupled systems that are predictable far into the future will behave very differently from loosely coupled ones.
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OR Opportunities: External view
Models and tools for planning and managing business to business services -Supply chain analogies - but without inventory as a lever
Models of human behavior-Workforce, customers
Event-based Decision Support -E.g. customized pricing, traffic conditions based routing,
Planning under uncertainty-Stochastic Optimization-Risk-based as well as expected value based
Business eco-system dynamics -Models for setting strategies and determining “next move”
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Business to Business Services
Environment characterized by -Relatively small number of large, long duration deals-Relatively few providers for any service-Within a deal multiple services, delivered as requested over time and geography-Complex deal prices, based on volume, service level and other factors-Shared resources serving multiple clients-On going automation and other cost take-out initiatives
Issues-Forecasting of deals (opportunities and/or signings)-Translating demand (deals) into requirements (resources) -Pricing deals (or supporting price negotiations)-Planning/acquiring capacity -Allocating scare resources-Estimating investment ROI
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Models of Human Behavior
In Service-based economies, and especially in industries based on human knowledge, the key resource to be “managed” is the individual human.-Supply chain models, based on large volumes of interchangeable parts, have limited
applicability-Skill taxonomies provide some useful data for short-term planning, but capture only
current skill, not attainable skills of relative distance between skillsTeam dynamics and multi-tasking lead to non-linear production functions-Data to quantify nonlinearities is not readily available.
There are few mature models of incentives, retention, workload-based performance, learning, etc.-And major cultural and generational differences exist.
Market-based models (individuals bidding for work assignments) have been proposed-Winner selection problem must capture complex constraints
Agent-based approaches have been proposed-But need good agent representations
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Social Network Analysis (SNA) Tool Example
Source: Rob Cross, McIntyre School of Commerce, University of Virginia, http://www.robcross.org/
Insights from SNA
- Organizational charts may not show how work really gets done
- Senior executives not always central
SNA can reveal problems:
- Central person could be overworked
- Risk to organization if central person goes away
- Peripheral people can represent untapped knowledge
Social network analysis reveals hidden connections providing an “organizational X-ray”
30 © Copyright IBM Corporation 2005
General Description of SNO
SNO (Social Network Optimization)-The application of mathematical algorithms to solve optimization problems related to information and communication networks of people
How does SNO relate to SNA (Social Network Analysis)?-SNA provides the input to SNO-SNO extends traditional SNA by considering additional graph theoretically based concepts (e.g. transmission dynamics. k-paths, reliable paths)-SNO extends the descriptive nature of SNA to prescriptive (optimization) decision support-SNA provides diagnosis to problems and improvement opportunities. SNO provides decision support for what to do next
How does SNO relate to Social Computing?-S.C. (e.g. wikis, blogs, email, IM, browser analyzers) provide a means of automating capture of SN data
From: Helander and Melachrinoudis (1997). Facility Location for Reliable Route Planning in Hazardous Material Transportation. Transportation Science, 31(3):216-226
SocialComputing
SNA
Data
SNOData
Surveys Data
EnableObserve, Describe, Diagnose
Optimize
OtherData
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Transaction-time Decision Support
Goal: optimal responses to events:-Use of more complete, more accurate, and more flexible
models of business systems-Use of real-time integrated data and computational power-Enables the generation and communication of timely
actionable decisions-May be embedded in automated process-May serve as advisor to human in the loop
Real WorldModel World
Data Analysis
LocalInfo
Decision Support
Example: Fleet Optimization
Challenge Planning and Dispatching coaches and drivers under dynamic traffic conditions and client requirements
Solution A planning and dispatching system based on start-of the art optimization, linked to reservation system and real-time data feeds.
Benefits Increased utilization of fleet and drivers (20%), improved customer satisfaction, and increased revenue (10%).
32 © Copyright IBM Corporation 2005
Planning under uncertainty
There is an increasing need to captures and characterizes variability and uncertainty in the outside world:-Extend models to include both internal variability and
external volatility due to environmental effects-Use predictive models and filtering techniques based on
real time data from multiple sources-Enable robust decision making, risk/rewards trade-offs
and consideration of recourse
Real WorldModel World
Stochastic Planning
StatisticalInfo
Data Analysis
LocalInfo
Event based Optimization
Evaluation of multiple demand patterns and pricing structures.Allows linkage to resource planning and deployment models
E-business on Demand PricingPrice
RevenueCost structure
Market structure
Costs
Profit
Demand
Multiplexing gain Capacity
Optimize with respect to price
Simulate with respect to demand
33 © Copyright IBM Corporation 2005
Modeling Eco-system Dynamics
Model a decision unit and the rest of the world as interacting entities:-Capture dynamic interactions within complex systems at
multiple timescales-Avoid negative effects of localized decision making, e.g.
Bullwhip Effect, Stock market fluctuations-Requires new methods for analysis, modeling and
optimization
Real WorldModel World
Collectiveand Dynamic
InfoAdaptive Optimization
Stochastic Optimization
StatisticalInfo
Data Analysis
LocalInfoEvent based
Optimization Electric Power Scheduling and Trading
The uncertainties in demand and prices over time together with the fixed grid topology and limited adaptability in the control mechanisms can result in local prices to vary over a very wide range. Adaptive control mechanisms that incorporate system dynamics can eliminate instabilities
0
28,000
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HoursM
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Demand Locality 1 Locality 2
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34 © Copyright IBM Corporation 2005
Adaptive Optimization
Signals ActionsEvaluate
Environment
Environment
The appropriate level for modeling and optimization depends upon one’s control points.
If you own the entire system, you can use a model of the entire system to determine actions which attain desired system behaviors.
If you only control one entity, then you can attempt to model the other players in the system, taking into account the likely effect of your own actions on them.
Evaluate
“Unit of control”
35 © Copyright IBM Corporation 2005
Links to Business Process Modeling
Emerging “science” around Business Process Modeling- model, design and transform Business Processes in a formal, structured way. - provide a structure that links a company's fundamental strategic mission with a hierachy of process models down
to an operational and I/T level. -
In some cases, leveraging new OR models requires that processes be transformed.- No data, no process to get the data, or no process for which decision makers can use the models
With knowledge of what OR models can do, business process modeling can drive from high level objectives and then to design of processes and and I/T flows that would enable a DSS/OR model.
The formalization of Process Modeling tools allows for OR to be embedded in the Process Models themselves
Business process modeling tools, for example, include simulation components to do performance testing of a Process.
Can add additional OR capability to allow things like risk assessment on top of the business process Can embed an operational OR model as a web service within a business process simulation to test and design various configurations of the process and the SCE model parameters.
36 © Copyright IBM Corporation 2005
Opportunities in Operations ResearchThere is a new opportunity to us Operations Research to provide value to industry- Model and analyze complex dynamics resulting from variability together with increasing speed, increasing
interdependence and shortening time-scales- Use these models and related analytic and optimization methods to support strategic, tactical and operational
business decisions- Collect, manage, and distribute the huge amounts of raw and analyzed data that describe the behavior of complex
business systems- Provide massive computational capability to businesses interested in exploiting advanced analytic and optimization
methods
In order to capitalize on this opportunity there must be close collaborations Operations Research and others in the Information technology industry- We must develop methods for both analyzing complex dynamics and computing unified optimal solutions across
multiple time scales and methods- We must provide methods for adaptive refinement of models and analysis methods as data, objectives or
environment changes- We must develop modeling and analysis techniques and algorithms that work efficiently on large-scale computing
infrastructures
IBM Research
Global Technology Outlook 2005 IBM Confidential / Do Not Distribute © 2004 IBM Corporation37
On Demand
Robust
Responsive
Controlled
Visible
Automated
Static Process Workflows
InformationRationalization
Automated Responses to events
Levels of Maturity
Dashboard Reporting
Transaction Processing
Workflows & Event Monitoring
Responsive Control
Impact to the Organization
As level of automation, information availability, and maturity increase, new uses for Operations Research emerge
Robust management
Adaptive business
Accommodate volatility/variability
Exploit environmental dynamics