Optimization DirectIntroduction & Recent Optimization Case StudiesInforms National ConferenceTechnology WorkshopHouston, October 2017

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

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

Exhibit Hall

• Booth #10

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

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: 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

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?

• 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

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

Why Optimization Direct?

• Experience

• Benchmark faster against competition

• Understand differentiators

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

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Seeds

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

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

Datascience Experience:Datascience.ibm.com

Jupiter Notebooks

• Machine Learning• Text mining• Deep learning• Preventive maintenance

• Optimization • Oil Blending• Unit commitment• Offer Optimization

To learn more

I will email the PDF of this powerpoint today.

Contact

Alkis Vazacopoulos201 256 7323 alkis@optimizationdirect.comwww.optimizationdirect.com