Industrial Algorithms LLC provides optimization solutions for advanced planning and scheduling problems. It develops the iMPress software for modeling and optimization of industrial processes. The company also provides consulting services and industrial modeling frameworks tailored for specific industries like oil and gas, consumer goods, and energy. Its goal is to help customers identify and solve bottleneck decision-making problems to improve bottom-line performance.

1. Introduction
Industrial Algorithms LLC.
Jeff Kelly & Alkis Vazacopoulos
July 18, 2013

2. Who we are?
– Jeff Kelly: 25-years of both production & process modeling &
optimization for planning, scheduling, control & estimation
(PSCE) problems in the process industries, worked in Shell,
Exxon, Honeywell, consulted for more than 30 companies.
– Alkis Vazacopoulos: 25-years of solving production planning
and scheduling in process, printing & publishing, consumers
goods, etc, worked for Dash Optimization, Fair Isaac, Verisk
and consulted for more than 100 companies.
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3. Our Mission
• To provide efficient solutions to solve complex
APS (Advanced Planning and Scheduling)
problems.
• Our primary focus is to implement smaller
applications with large benefits versus installing
a large application with small benefits. To do
this, we help you identify your worst decision-
making bottlenecks and provide solutions
targeted to reducing their negative impact on
your bottom-line.
3

4. 4
What We Do
• iAL develops and markets iMPress, the
world’s leading software product for
flowsheet modeling and optimization.
• iAL provides in-house training for
customers along with complete software
support and consulting.
• iAL provides Industrial Modeling
Frameworks (iMf’s) for several problem
types.

5. Our Mandate
• To provide advanced modeling and solving
tools for developing industrial applications in
the decision-making and data-mining areas.
• Our targets are:
– Operating companies in the process industries.
– Application software providers.
– Consulting service providers.

6. Our Industrial Modeling
Frameworks
• Process industry business problems can be
complex hence an Industrial Modeling
Framework provides a pre-project or pre-
problem advantage.
• An iMf embeds Intellectual Property related
to the process’s flowsheet modeling as well
as its problem-solving methodology.

7. What type of iMFs we have
developed
• Jet Fuel Supply Chain Design with Refinery and
multimodal transportation mode
• Maritime Shipping Supply Chain Design
• Real Time Blend Optimization
• Pipeline Scheduling Optimization
• Fast Moving Consumer goods – Planuling
Optimization
• Capital Investment & Facilities Location
• Advanced Production Accounting
• Advanced Process Monitoring
• Advanced Property Tracking/Tracing
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8. Our Business Model
• What do we license: iMPress, iMf, 3rd
Party Solvers.
• What is our pricing scheme: License
Fee, Support & Maintenance Fee.
• What are our license terms: Based on
Customer’s Needs i.e., Rental for a
specified period (months to years) or
Perpetual.

9. 9
iAL Services
• Application Support
– Free prototyping to get you started (for a
reasonable number of consulting hours).
– Full modeling and solving support.
• Consulting Services

10. 10
iAL Facts
• Founded 2012
• Offices in the US (New Jersey), Canada
(Toronto)

11. 11
Industrial Algorithms
Differentiators
• Modeling Focus
• Optimization Focus
• No Competition with
Consultants and OEMs
• Customer Support
• Solution of Hard Problems
• Flexible Licensing Terms

12. Academic Collaboration &
Partnership
• Carnegie Mellon University
• University of Wisconsin
• Stevens Institute Of Tech
• Fairleigh Dickinson University
• George Washington University

13. 13
Academic Partnership Program
• Free Full-Edition iMPress licenses for degree-
awarding institutions for research and
teaching

14. 14
Working with Customers –
Current Projects
• Pipeline Optimization with DRA.
• Refinery Planning and Scheduling.
• Fast Moving Food Industry Planning and Scheduling.
• Jet Fuel Supply Chain.
• Beer Supply Chain Planning and Scheduling.
• Gasoline Blend Monitoring with ProSensus.
• Data Reconciliation Engine embedded in TUVienna STAN
Software.

15. 15
Development Directions
Performance
Ease of Use
Problem Types

16. 16
Development Directions
Performance
Ease of Use
Problem Types

17. We solve problems that deal with the
following decisions:
Quantity
How much to produce?
What is the batch-size?
Quality
How to blend specific
products to satisfy certain
levels of quality?
Logic
What machines to use?
How to sequence the jobs to
minimize setup costs?
Time
When to produce?
How to respect past
decisions & future orders?
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18. We solve these types of problems
Our system can model and
solve problems which are a
mix of both
planning & scheduling
decision-making.
We introduce nonlinear
optimization in large-scale
planning and scheduling
problems and
solve problems involving
quantity, logic & quality.
We properly manage
complexity in problems that
would normally be considered
as uncertainty by other
vendors.
We use data-mining
techniques to support the
solving of problems that
incorporate control,
feedback, and
maintainability issues.
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19. 19
Development Directions
Performance
Ease of Use
Problem Types

20. How do we model the Superstructure?
 Configure versus Code:
 Draw the flowsheet of connected industrial objects and
the sets, parameters, variables, constraints &
derivatives are automatically created.
 User, custom or adhoc sub-models can also be coded
when required.
Unit-
Operation 1
Unit-
Operation 2
Port-State 1
Port-State 2
charge, batch & lot-sizing,
input-output yields,
stream flow bounding,
min/max run-lengths & cycle-times,
sequence-dependent setups,
certification delays,
density, composition & property limits,
nonlinear & discontinuous formulas,
economic, environmental & efficiency
objectives, etc.

21. Why are we unique?
• iMPress is flowsheet-based (i.e., a figurative
language).
– This means that the modeling is inherently network or
superstructure “aware” with equipment-to-
equipment, resource-to-resource, activity-to-activity,
etc. as explicit language constructs or objects.
– It also means that all of the effort of generating the
sparse A matrix in the LP, MILP and NLP is done
automatically by automatically creating all of the sets,
parameters, variables and constraints when the
model is configured using our proprietary and
comprehensive library of sub-models.
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22. Why are we unique?
• iMPress is “shape-based” which is
different from other modeling systems:
– Algebraic modeling languages like GAMS,
AIMMS, AMPL, etc. are “set-based”.
– Applied engineering modeling languages
like ACM, gPROMS, APMonitor, NOVA-MS,
Modelica, etc. are “structure-based”.
– Array manipulation modeling languages like
Matlab, Mathematica, Octave, etc. are
“scalar-based”.

23. Jet Fuel Supply Chain iMf

24. How do you configure problems?
• Problems are configured either:
– Interfacing with our flat-file Industrial Modeling
Language (IML) or
– Interactively with our Industrial Programming
Language (IPL) using a programming language
such as C, C++, C#, Java, Python, etc.
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25. 25
Development Directions
Performance
Ease of Use
Problem Types

26. Performance Issues
• Solve Large-Scale Problems.
• Take Advantage of MILP technologies
and multiprocessors.
• Efficient Memory Management.
• Strong Presolving.
• Nonlinear Technology that can handle
complex NLP problems.
• Support for decomposition/polylithic
modeling. 26

27. What Math Programming and Solvers
we use?
Supply-chain planning and
scheduling optimization problems,
Logistics modeling and solving is
required utilizing Mixed-Integer
Linear Programming (MILP).
Production-chain planning and
scheduling optimization problems, both
Logistics and Quality optimization
models are solved using an integrated
and innovative combination of both
MILP and Nonlinear Programming
(NLP).
We currently have bindings to several linear and nonlinear
programming solvers such as
COINMP, GLPK, LPSOLVE, SCIP, CPLEX, GUROBI, XPRESS,
XPRESS-SLP, CONOPT, IPOPT,
KNITRO & IMPRESS-SLPQPE.
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28. Real Time Blend Optimization iMf
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29. Fast Moving Consumer Goods iMf
Bulk-Line
Pack-Line
Sequence-
Dependent
Switchovers
Forecasted &
Firm Future
Demand Orders

30. • Time Horizon: 60 time-periods w/ day periods.
• Continuous Variables = 10,000
• Binary Variables = 5,000
• Constraints = 20,000
• Time to First Good Solution = 10 to 30-
seconds
• Time to Provably Optimal = 1 to 10-hours due
to sequence-dependent switchovers.
• Solver: Tested with Xpress & Gurobi
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Fast Moving Consumer Goods iMf

31. Cogeneration (Steam/Power) iMf
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• Time Horizon: 168 time-periods w/ hour
periods.
• Continuous Variables = 5,000
• Binary Variables = 1,000
• Constraints = 7,500
• Time to First Good Solution = 5 to 30-
seconds
• Time to Provably Optimal = 5 to 15-minutes

32. Cogeneration (Steam/Power) iMf
Water
Pump

33. • Time Horizon: 168 time-periods w/ hour
periods.
• Continuous Variables = 5,000
• Binary Variables = 1,000
• Constraints = 7,500
• Time to First Good Solution = 5 to 30-seconds
• Time to Provably Optimal = 5 to 15-minutes.
• Solver: CPLEX
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Cogeneration (Steam/Power) iMf

34. Power Generation iMf
• Three thermal-plants and two hydro-plants
with and without water storage.
• Three nodes or buses with voltage phase
angle inputs where each bus obeys
Kirchhoff’s current and voltage laws.
• One time-varying demand load located on
bus #3.

35. Power Generation iMf

36. Capital Investment/Facilities Location iMf
Expansion?
Installation?

37. Maritime Industrial Shipping iMf
Inventory
Routing

38. SubsTance flow ANalysis (STAN) iMf
• Large-scale data reconciliation and regression is
performed to compute observability, redundancy
and variability estimates.
• Substances are any material or meta/sub-material
(concentrations) which need to be traced within the
flowsheet or network to track their movements
based on flow and composition measurements over
time.
• STAN is a software development from TUVienna
using iAL’s iMPress solver called SECQPE
(successive equality-constrained QP engine).
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SubsTance flow ANalysis (STAN) iMf

40. Other uses of IMPRESS …
• First-principles or rigorous process modeling to
manage difficult but high-valued bottlenecks.
• On-line process/production monitoring to compare
model predictions with plant actuals in real-time.
• Large-scale nonlinear optimization to solve
industrial scale problems where there is a large
portion of linear constraints and a smaller portion of
nonlinear constraints with multilinear cross-product
terms (x1*x2) using successive linear & quadratic
programming.
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41. Linking iMPress with
IBM/ILOG ODME & Cplex (work
with DecisionBrain)
System Architecture

42. ODME-IMPRESS-CPLEX
System Architecture
• A domain-specific data model was created in
ODME using the usual master-data and
transactional-data partitions.
• A mapping between iMPress’ data model and
ODME’s data model was established.
• Java code was written to export iMPress’ IML
file (Industrial Modeling Language).
• SWIG Java was used to create a Java Native
Inerface (JNI) to iMPress.

43. ODME-IMPRESS-CPLEX
System Architecture
• Java code was written to call iMPress-CPLEX
using its API’s.
• Java code was written to access the solution(s)
from iMPress-CPLEX using its API’s and to
populate the ODME solution-data partition.

44. ODME Screen Shots

45. Data-Model in ODME

46. Master-Data

47. Transactional-Data

48. Gantt Chart for Reference (Base)

49. Trend Plots for Reference (Base)

50. Demand Variability Scenario Data w/
Reference in ()

51. Trend Plots for Demand Variability
Scenario w/ Reference

52. Benefits
• Perfectly fit your business model and decision processes
• Sophisticated optimization capabilities able to tackle complex,
non-linear and large-scale problems
• A solution that can be quickly adapted to new production
processes
• A user-friendly GUI to help planners driving refinery operational
excellence and analyzing refinery behavior
• What-if scenario analysis for confident decision-making
• See all your data and options in one place with drill-downs and
graphics
• Collaborate with other planners
• Powered by IBM ILOG CPLEX Optimizers

53. How do we engage?
• We first consult to determine how we can
improve the profit and performance of the
problem as a whole.
• Then, depending on the benefit areas and
apparent bottlenecks, a tailored and
incremental solution is implemented which
focuses on both improving economics and
increasing efficiency whilst being
transparent and usable.
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54. How do we engage?
• Using our Industrial Modeling Frameworks
(IMF): These are preconfigured solutions
that we can adopt to your specific
problems.
• We have IMFs in the following areas:
– Production Planning
– Plant Scheduling
– Pipeline & Marine Shipping
– Energy Management
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55. For a demonstration of our IMFs
& IMPRESS, please Contact
• Alkis Vazacopoulos
• Industrial Algorithms LLC
• Mobile: 201-256-7323
• alkis@industrialgorithms.com
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