odinc informs houston 2017 av - optimization direct · analytics –gartner report • data science...
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Optimization DirectIntroduction & Recent Optimization Case StudiesInforms National ConferenceTechnology WorkshopHouston, October 2017
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Agenda
• Alkis Vazacopoulos: ODINC review and a quick introduction to Datascience Experience
• Sumeet Parashar, IBM: Decision Optimization (CPLEX) in DSX using Python Notebooks - An introductory example
• Ed Klotz, IBM: Automatic Benders Decomposition in CPLEX
• Robert Ashford: Recent benchmarking results with ODh+CPlex
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Technology Tutorial
• October 22
• 11:00-12:30
• 372F
• An Overview of DSx: Datascience Experience for Advanced Modeling and Optimziation and Latest Development in CPLEX and Odh+CPLEX
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Exhibit Hall
• Booth #10
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Optimization Direct
• IBM Business Partner• More than 30 years of experience in developing and
selling Optimization software• Sold to end users – Fortune 500 companies• Train & Help our customers to get the maximum out of
the IBM software
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What software do we sell?
• IBM ILOG CPLEX Optimization Studio
• DOCPLEXCloud (Cloud offering for CPLEX) • Cplex is the leader in optimization technology• Cplex can handle large scale problems and solve them very
fast
• SPSS• SPSS is the leader in Predictive Analytics
• DSX • Datascience Experience • Datascience.ibm.com
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Which markets & new platforms
• Big DATA: Sparc & Hadoop & Python
• Linking optimization with Data science Projects (Predictive & Prescriptive) – DATA SCIENCE EXPRERIENCE PLATFORM
• Travel, Hotel, Cruises
• Retail, Groceries, Clothing
• Energy, Renewables, Process
• Financial, Banking
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Why IBM? Why Cplex?
• Fast (Very fast) & Reliable
• IBM software (Cloud an on Premise offerings)
• Large scale Optimization
• Gives you the ability to model develop and solve your decision problem (Modeling tools)• Complete solution (Modeling & Solver)
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What types of problems?
• Big Data: We see new innovations in human /machine interface and how operation research Experts they solve complicated problems in data mining• Deep Learning • Support Vector Machines
• Price & revenue optimization (Travel Industry, etc..,)
• Retail – optimization of campaigns
• Financial: trading, portfolio optimization
• Process industries: schedule your refinery
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How can we help?
• Benchmark your problems• MPS matrices• OPL models• C, C++ code• Rstudio• Python • Concert Technology• Constraint programming
• Develop optimization prototypes using OPL
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Why Optimization Direct?
• Experience
• Benchmark faster against competition
• Understand differentiators
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Recent Analytics & Optimization Case Studies
• Big Data – Pricing – Hadoop + CPLEX
• Hospital (OPL MODEL + MIP)
• DNA Screening Company (MIP + CP)
• Workforce scheduling Problem (CPLEX + ODH)
• Sports (MIP, MIP + Local Search, Regression)
• Customized Offers Company (Analytics + MIP)
• Packaging and Fulfillment (MIP, MIP+CP)
• Pharma Co (Analytics, Robust Opt, MIP)
• Energy Co (MIP, extend to Stochastic MIP)
• Financial company (Complex QCPs, MIP)
• Retail Clothing (Analytics, MIP)
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DNA Screening - Scheduling problems –Constrained Programming
• New Innovative DNA Screening Companies
• Goal: Make custom-built robots to turn blood and saliva samples into purified DNA.
• Samples: These samples come from men and women across the globe.
• DNA Sample and Robots: The robots can analyze thousands of DNA samples at the same time, and can work nonstop seven days a week.
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DNA Screening Problem
• This is Flowshop scheduling problem with Many Side Constraints
• Challenge: Increase Utilization of the robots –decrease idle time
• Solver: Constrained programming
• Time Horizon: Determine easily Daily sequences and develop a rolling horizon schedule
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Workforce Scheduling – ODHeuristics & CPLEX
• Schedule entities over 64 periods
• Many Side constraints
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ODH Case: Worksforce Scheduling Example: Large Scale Scheduling models
• Schedule entities over 64 periods
• No usable (say within 30% gap) solution to small model after 3 days run time on fastest hardware (Intel i7 4790K ‘Devil’s Canyon’)
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Solution: ODH & CPLEX
• Uses CPLEX as a solver
• Solves sequence of sub-models
• Delivers usable solutions (12%-16% gap)
• Takes 4-36 hours run time
• Multiple instances can be run concurrently with different seeds
• Can run on only one core
• Can interrupt at any point and take best solution so fartime limit / call-back /SIGINT
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Large Model Heuristic Behavior
1020
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0 10000 20000 30000 40000 50000 60000 70000
Solu
tion
valu
e
Time in seconds
12345678901221098
Seeds
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October 2017: Latest Release ODH
• ODH is a solver (more RWA’s talk)• Works with CPLEX
• Users:• Large CO: Uses ODh for more than 2 applications • AIMMS resells ODh
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Analytics – Gartner Report
• Data Science & Analytics is the main focus in most of the Fortune 1000 Companies
• IBM has a clear path for combining • Data Science • Predictive • Prescriptive • Congitive
• Analytics• Cloud & on premise
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Datascience Experience:Datascience.ibm.com
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Jupiter Notebooks
• Machine Learning• Text mining• Deep learning• Preventive maintenance
• Optimization • Oil Blending• Unit commitment• Offer Optimization
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To learn more
I will email the PDF of this powerpoint today.
Contact
Alkis Vazacopoulos201 256 7323 [email protected]