odinc informs lv 2017 av - optimization direct
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Optimization DirectIntroduction & Recent Optimization Case StudiesInforms Business Analytics ConferenceTechnology WorkshopLas Vegas 2017
Agenda
• Alkis Vazacopoulos: Introduction & Case Studies, Combining Predictive and Prescriptive Analytics
• Vincent Beraudier: Develop a Data Science project for a Marketing campaign planning with Python and CPLEX.
• Robert Ashford: Recent computational experience with ODH-CPLEX
• Yiannis Gamvros: Industrial Maintenance Scheduling: Challenges, Solution, Benefits
Technology Workshop
• Technology Tutorial: An Introduction to ODH-CPLEX and Recent Computational Results
• Optimization DirectMonday, April 3, 9:10-10amLocation: Octavius 21
Optimization Direct
• IBM Business Partner• More than 30 years of experience in developing and
selling Optimization software• Experience in implementing optimization technology in
all the verticals• Sold to end users – Fortune 500 companies• Train our customers to get the maximum out of the IBM
software• Help the customers get a kick start and get the
maximum from the software right from the start
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
Which markets & new platforms
• Big DATA: Hadoop & Sparc
• Linking optimization with Data science Projects (Predictive & Prescriptive)
• Travel, Hotel, Cruises
• Retail, Groceries, Clothing
• Energy, Renewables
• Financial, Banking
• Process industries
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)
What types of problems?
• Price & revenue optimization (Travel Industry, etc..,)
• Retail – optimization of campaigns
• Financial: trading, portfolio optimization
• Process industries: schedule your refinery
• 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
How can we help?
• Benchmark your problems• MPS matrices• OPL models• C, C++ code• Rstudio• Python • Concert Technology• Constraint programming
• Help you with next steps for developing your solution!
• Develop optimization prototypes using OPL
Why Optimization Direct?
• Experience
• Responsive
• Benchmark faster against competition
• Expertise
• 15 years of experience competing with CPLEX
• Understand differentiator
• Know how to sell against competitors
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)
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.
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
Workforce Scheduling – ODHeuristics & CPLEX
• Schedule entities over 64 periods
• Many Side constraints
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’)
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
Large Model Heuristic Behavior
1020
1040
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1080
1100
1120
1140
1160
1180
0 10000 20000 30000 40000 50000 60000 70000
Solu
tion
valu
e
Time in seconds
12345678901221098
Seeds
April 2017: Release ODH
• ODH is a solver (more RWA’s talk)
• Works with CPLEX
• Users:• Large CO: Uses ODH for more than 2 applications
Pricing ODH
• You will need to have CPLEX
• Server: • For 8 cores: $3,000 • For 16 cores $6,000 etc….
• (price Includes maintenance for first year)
• Maintenance: 30% after first year
• For many applications Benefits much higher than cost
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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