Optimization Direct. Introduction & Recent Optimization Case Studies Informs Business Analytics Conference Technology Workshop Las Vegas 2017

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1 Optimization Direct Introduction & Recent Optimization Case Studies Informs Business Analytics Conference Technology Workshop Las Vegas 2017

2 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

3 Technology Workshop Technology Tutorial: An Introduction to ODH-CPLEX and Recent Computational Results Optimization Direct Monday, April 3, 9:10-10am Location: Octavius 21

4 Exhibit Hall Booth #9

5 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

6 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

7 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

8 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)

9 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

10 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

11 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

12 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)

13 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.

14 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

15 Workforce Scheduling ODHeuristics & CPLEX Schedule entities over 64 periods Many Side constraints

16 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 )

17 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 far time limit / call-back /SIGINT

18 Large Model Heuristic Behavior Solution value Seeds Time in seconds

19 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

20 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

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22 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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24 Datascience Experience: Datascience.ibm.com

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27 To learn more I will the PDF of this powerpoint today. Contact Alkis Vazacopoulos alkis@optimizationdirect.com