Decision Optimization - CPLEX Optimization Studio - Product Overview(2).PPTX

© 2021 IBM Corporation
IBM Data and AI
IBM ILOG CPLEX Optimization Studio
(COS)

IBM Data and AI
Why Prescriptive Analytics?

3
IBM Data and AI
Prescriptive Analytics and Industry Leaders
vs.
vs.
vs.
vs.
vs.
Source: The Optimization Edge, Steve Sashihara (New York, NY: McGraw Hill, 2011) p. 3

4
IBM Data and AI
Prescriptive Analytics Solutions – Documented Results
2 Chilean Forestry firms Timber Harvesting $20M/yr + 30% fewer trucks
UPS Air Network Design $40M/yr + 10% fewer planes
South African Defence Force/Equip Planning $1.1B/yr
Motorola Procurement Management $100M-150M/yr
Samsung Electronics
Semiconductor
Manufacturing
50% reduction in cycle times
SNCF (French Railroad) Scheduling & Pricing $16M/yr rev + 2% lower op ex
Continental Airlines Crew Re-scheduling $40M/yr
AT&T Network Recovery 35% reduction spare capacity
Grantham Mayo van Otterloo Portfolio Optimization $4M/yr
Source: Edelman Finalists, http://www.informs.org or http://www.scienceofbetter.org

5
IBM Data and AI
Prescriptive Analytics within the Analytics Portfolio
COGNITIVE
Have you
considered?
Understand
Reason
Learn
What is
happening in
my business
today?
DESCRIPTIVE
Discover
Report
Analyze
What will
happen in
the future?
PREDICTIVE
Forecast
Correlate
Anticipate
What should I
do about it?
PRESCRIPTIVE
Recommend
Decide
Assign
Technology
Need
Outcome

6
IBM Data and AI
What Is Prescriptive Analytics?
 Manage resource efficiency, utilization and allocation
 Resources can be a number of things, in different industries
 Keywords to look out for:
 Minimize and maximize
 How many, how much, which, when, where
 Decide, choose, plan, schedule, assign, route, source, maintain, locate, trade-
off
Resources Industries
Capital Financial
People
Industrial, Public, Comms, Distribution,
Financial
Equipment Industrial, Comms
Facilities Distribution, Industrial
Vehicles Distribution
Material/Product Distribution, Industrial

IBM Data and AI
Quick Product Review

8
IBM Data and AI
Prescriptive Analytics – How Does It Work?
Inputs typically come
from ERP systems, siloed
DBs or entered manually
1
In many cases ”Demand
to be Met” comes from a
Predictive Analytics
Solution like SPSS
2
The model is typically
created by a “quant” and
is specific to the business
problem and industry
3
The engine is the run-
time component of the
IBM Decision
Optimization software
4
Output is typically written
back to an ERP or a DB
and can be consumed
through a BI solution
5

9
IBM Data and AI
What Is an Optimization Model?
A translation of your business problem into math
Business goals /
KPIs
Increase profits
Model objectives
Maximize Profit
Levers / decisions
affecting goals
How much product A
should we produce?
Model variables
Production_ProductA
Business “rules”
constraining
decisions
Production must be less
than plant capacity
Model constraints
Production_ProductA
≤ Capacity_A

IBM Data and AI
Product Details of
IBM ILOG CPLEX Optimization Studio
(COS)

11
IBM Data and AI
COS – Target Audience
 Operations Research (or Data Scientist) Professional:
 Typically math degree in Operations Research, Applied Mathematics, Industrial
Engineering etc.
 Primarily interested in creating the mathematical-optimization model to solve a
business problem
 Wants to work either with a modeling environment or with engine APIs
 Expects appropriate data (demand, resources, business rules) to be available for
use
 Expects IT to integrate incoming data, with the execution of the optimization model
and for writing results back into appropriate IT systems (e.g., ERP)
 IT Professional (as long as they are embedding the models what the OR
professional has created):
 Typical system integration and sysadmin skills as well as some DB API
programming skills
 Wants make appropriate data sources available for optimization model
 Wants to run optimization engine (i.e., COS) in a stable architecture
 Wants to write results back to appropriate IT system

12
IBM Data and AI
COS – Key Features and Capabilities
Model development Tools
Optimization Engines
Math Programming
CPLEX Optimizers
Constraint Programming +
Constraint-based Scheduling
CP Optimizers
OPL Studio IDE - OPL Modeling Language
ILOG Concert Technology (C++, .NET, Java, Python) for embedding (and modeling)
Connectors
•Microsoft Excel
•MSF
•MATLAB
•AMPL
•Python
Tools & APIs
•CPLEX Interactive
•C Callable Library
CPLEX Optimization Studio (COS)
Cloud solve
from the IDE

13
IBM Data and AI
1
2
3
4
5
6
0
50
100
150
200
250
300
350
12.4
(2011)
12.5.0
(2012)
12.6.0
(2013)
12.6.1
(2014)
12.6.3
(2015)
12.7.0
(2016)
12.8.0
(2017)
12.9.0
(2018)
12.10.0
(2019)
20.1.0
(2020)
total
speedup
number
of
timeouts
CPLEX MILP performance evolution
 10 sec
 100 sec
 1000 sec
Date: 17 December 2020
Testset: MILP: 4797 models
Machine: Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz, 64 GB RAM, 12 threads, deterministic
Timelimit: 10,000 sec

14
IBM Data and AI
Optimization Engines – CPLEX – Overview
 Roots in Analytics Geometry and Matrix Algebra
 Used to solve strategic and tactical resource allocation questions
where optimality is important.
 Typical applications
 Large-scale.
 High performance.
 Mission critical.
 Versatile: embeddable & stand-alone.
 Sophisticated analysis.
 Problem Types:
 Linear (LP), Mixed-Integer (MIP) and (convex/non-convex) Quadratic (QP,
QCP, MIQP, MIQCP), SOCP
 What is inside?
 Simplex optimizers (Primal, Dual, Network), Barrier Optimizer
 Branch-and-Cut algorithm
 Heuristics (Genetic algorithms, solution polishing, neighborhood search etc.)

15
IBM Data and AI
Optimization Engines – CPLEX – Key Features
 Parallelization of Algorithms
 Takes advantage of multiple cores and speeds up search (shared memory
architectures)
 “remote object” API to allow parallelization on distributed memory architectures
 Infeasibility Analysis
 Detects minimal set of constraints that causes infeasibility
 Provides recommendations for relaxations that will fix infeasibility
 Solution Pool
 Generation of multiple solutions for every problem
 Alternative optimal solutions or solution within a % of optimality
 Modeling assistant
 Scan the model from many aspects and gives “advices” how to improve or where are
issues with the model instance
 Parameters and Callbacks
 Extensive set of parameters that experts can fine tune
 Callback code written by experts can be executed at runtime together with native code

16
IBM Data and AI
Optimization Engines – CPLEX – Key Concepts
Optimality Gap
 How far away is my current solution from the best possible solution?
TIME
SOLUTION
COST
Current Solution
Theoretical Bound
Large gap
indicating a poor
solution.
As the search
progresses the
gap is reduced.
When the solution and the
theoretical optimal become
the same we have proof of
optimality.

17
IBM Data and AI
Optimization Engines – CP Optimizer – Overview
 Typical Applications:
 Complex feasibility problems and detailed scheduling problems.
 Problem Types:
 Almost no restrictions except discrete decision variables (i.e., 1,2,3 as
opposed to ranges [1-3])
 Key Features:
 Scheduling-specific modeling constructs
 Powerful default search for a model-and-solve approach
 Custom search capability can complement default search.
 Parallelization - takes advantage of multiple cores and speeds up search

18
IBM Data and AI
OPL Studio IDE – Model Development Tools
Inspector/output tabs
Model/script code editor for
CPLEX and CP Optimizer models
Problem outline
Problem browsers
Project navigator

19
IBM Data and AI
An Example of a CPLEX (MIP) Model
Data initialization
Decision Variables
Objective Function
Post-processing
Constraints

20
IBM Data and AI
An Example of a CP Optimizer (scheduling) Model
Data initialization
Decision Variables
Objective Function
Constraints

21
IBM Data and AI
Accessing the Engine – Alternatives
 Important IBM Connectors:
 SPSS Modeler dev connector (Introduction, Additional Video 1, Additional
Video 2)
 Planning analytics connector
 Other Connectors:
 Microsoft Excel (deprecated)
 JDBC connector in the IDE
 Microsoft Solver Foundation
 MATLAB – Modeling IDE for several math function and engines
 CPLEX Interactive – Executable that runs in console
 APIs:
 Java, C++, .NET,
 Python
 C Callable Library
 OPL has embedding API’s in Java, C#, C++ and DOOPL (Python)

22
IBM Data and AI
Deployment of Optimization Solutions
 Through CPLEX/CPO/OPL (embedding) APIs:
 IT can use the native language of the overall solution (C, Java, C++, .NET,
Python) to call the code (or model) created by the OR professional
 All that is required by IT is to link the appropriate library with the COS native
code during compilation
 No other deployment restrictions exist - COS can practically run anywhere!
 IBM Decision Optimization Center (DOC)*:
 DOC has server technology out of the box
 Servers are built on dockerized images
 DOC provides Angular-based LOB-facing web-GUI tool builder
 For modeling DOC includes CPLEX Optimization Studio
*: separately purchased, i.e. DOC includes COS, but COS doesn’t include any component of DOC (like server, web-GUI building tool, etc)

IBM Data and AI
Thank you!

24
IBM Data and AI
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