INFORMS Metro NY February 2013
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![Page 1: INFORMS Metro NY February 2013](https://reader035.fdocuments.net/reader035/viewer/2022081400/554a3b81b4c905863d8b4a47/html5/thumbnails/1.jpg)
NEXT GENERATION OF
MATHEMATICAL PROGRAMMING
MODELING AND SOLVING TOOLS
NEW YORK INFORMS METRO CHAPTER
Alkis Vazacopoulos
Industrial Algorithms
February 2013
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Agenda
Optimization Problems (OP)
How do we solve OP?
Can we Graph and Solve?
Case Studies
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Solving a Business Problem with Optimization****
Business Problem
Mathematical Model
Solver
Supply Chain Optimisation Programme RASA Benefit Realisation Weekly Summary
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Realised Benefit Missed Opportunity Actual ≠ QS61 or Optimal
Business Results
min cTx
s.t. Ax b
x integer
x1 = 3, x2 = 0, ...
Solution to Mathematical Model
OR Specialist
Business Expert Ev
alu
ation
What are the key decisions?
What are the constraints?
What are the goals?
From: Jean-François Puget, IBM Distinguished Engineer,, recent presentation, IBM: Lessons Learned When Selling
Optimization To Business Users
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Business users
They don’t care about the
technology
They care about their problem
Eg schedule next day plant
operations, next month roster for
bus drivers, etc
They want
Return on investment
Help to solve their problem
To be in charge
Business Problem
Supply Chain Optimisation Programme RASA Benefit Realisation Weekly Summary
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Realised Benefit Missed Opportunity Actual ≠ QS61 or Optimal
Business Expert Ev
alu
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Business Results
From: Jean-François Puget, IBM Distinguished Engineer,, recent presentation, IBM: Lessons Learned When Selling Optimization
To Business Users
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Return on investment for optimization is great
From: Jean-François Puget, IBM Distinguished Engineer,, recent presentation, IBM: Lessons Learned When Selling
Optimization To Business Users
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What are common solving optimization
technologies?
Linear Programming
Mixed Integer Programming
Nonlinear Programming
Constrained Programming
Meta-Heuristics
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Access the solver: Use the Libraries
Load the matrix Using C
Visual Basic
Python
other
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Access the solver: Use a modeling language
Mathematical Programming language (basically
they do the same thing):
GAMS,
AMPL
AIMMS
MOSEL
MPL
OPL
LINDO
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Question #1
Can we automate the model formulation?
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Our Goal: Automate the process of
matrix generation
Literature
EMOSL (Dash Optimization)
CONCERT (ILOG)
Semantic/Structured Modeling (Professor A. Geoffrion)
OPTEX – DecisionWare
UOPSS (Jeff Kelly)
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Our Modeling Environment: IMPRESS
IMPRESS: Industrial Modeling & Presolving System is our proprietary modeling platform.
You can model, solve, interface and interact with any supply-chain, production-chain, demand-chain and/or value-chain optimization problem.
IMPRESS so far has been applied in:
Production Planning
Plant Scheduling
Pipeline & Marine Shipping
Energy Management
2/11/2013
12
Copyright, Industrial Algorithms LLC
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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.
2/11/2013
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Copyright, Industrial Algorithms LLC
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Jet Fuel Supply Chain IMF - Example
One oil-refinery producing different grades of jet
fuel and one airport terminal storing Jet-A, Jet-A1
and Jet-B with a railroad in between.
Logistics details such as the input-output or yield
modeling of the refinery and the round-trip times of
the tank-cars (similar to batch-processes with cycle-
time) are modeled & solved as a MILP.
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Jet Fuel Supply Chain IMF - Flowsheet
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Jet Fuel Supply Chain IMF - Flowsheet
Simple Structure of the
Oil Refinery
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Jet Fuel Supply Chain IMF - Flowsheet
Tank Cars and Airport
Jet Tanks
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Jet Fuel Supply Chain IMF - Flowsheet
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Question #2
What type of Units can I represent in the flowsheet?
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Jet Fuel Example - UNITS
Supply
Point
Outlet port – flow
interface
Tank, Pool Crude Oil distillation Unit
Vacuum
Distillation Unit
Demand
Point
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Question #3
How can you add a Unit in IMPRESS?
What about a general purpose modeling
language?
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Easy to add or insert Units
Add another tank in parallel
Add a jet fuel parcel (movable inventory)
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What about adding in the modeling Language?
You need to add both
the node set and arc
set in the modeling
structures
Variables
Index sets
Data
Constraints
Objective
function
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Comparison
MODELING
Languages
IMPRESS
Can you model this problem? Probably YES
How Long it will take? Cost? 6 months
1-2 consultants
days
What type of consultant?
OR Specialist Engineer or OR
Specialist
What about Nonlinear
Constraints ?
A few modeling
Languages
YES
Multi-Period You have to do
this yourself
Standard
What happens if I want to
delete or add a unit ?
Remodel and
Reprogram
Add or Delete
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Question #4
How do you draw a chart?
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How do we draw the chart
Use DIA Python, Open Source [Microsoft Visio]
IML, Data File
IPL, API
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DIA Chart
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Question #5: Why this technology
now?
No recent innovation in Modeling Languages
Mixed Integer Programming Codes are Very Fast
(Gurobi, CPLEX, Xpress)
Engineering Disciplines use optimization, but they are
not OR Specialists (they have little modeling
expertise and MP expertise)
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Question #5: Why this technology
now?
Most of the difficult Planning and Scheduling
problems have Special Structures (Electrical Eng.
and Chemical Eng. : Power flow network, Process
flowsheet, OR specialist have limited experience in
modeling them).
Engineers know about the modeling structure but
have limited MP algorithmic experience .
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Power Flow Network
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Process Flow Network
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Planning and Scheduling Model
Logistics (Quantity*Logic (proy’d-quality), MILP):
“discrete-time” where each time-period has the same duration.
Time-periods may be “small-buckets” (un-ary) or “big-buckets” (N-ary):
If un-ary then only one activity per time-period (scheduling) but if N-ary then multiple activities per time-period where a “time-portion” variable for each operation is applied (planning).
Quality (Quantity*quality (fixed-logic), NLP):
“distributed-time” where each time-period may have a different duration (global/common time-grid).
Same as logistics.
All input data is entered in “continuous-time” (begin, end-times) and digitized i.e., discretized or distributed accordingly.
Planuling = Planning + Scheduling
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Scheduling
planning
crude
without the blender scheduling details
Solution: duration of each blender grade with
time-period
Scheduling only for the blender:
Including setup, startup,
switchover, shutdown
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Crude A
Crude B
Crude C
Crude D
Prod A
Prod B
Prod C
Prod D
Prod E
Prod F
Prod
G
Prod H
Crude Recipe
Crude Scheduling
Refining Units
Operational Modes
Products Recipes
Products Planning
Products
Scheduling
Including setup, startup, switchover, shutdown
resulting targets to
blender Scheduling/RTO
Prod A
Prod B
Prod C
Prod D
Prod E
Prod F
Prod
G
Prod H
1st Planuling (big-buckets) 2nd Scheduling
(small-buckets)
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Engineer
IMPRESS
Engineer
Engineer
Engineer
Supply Chain Optimisation Programme RASA Benefit Realisation Weekly Summary
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Co
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8 Q
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1 B
aselin
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£k)
Realised Benefit Missed Opportunity Actual ≠ QS61 or Optimal
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A planning and scheduling example:
Fast Moving Consumer Goods IMF
Two materials produced in bulk-unit produces
eighteen different packaged materials in pack-unit.
Sequence-dependent switchovers with
setup/setdown times & “repetitive” maintenance
cleanouts on bulk-unit with material families.
Due to the slow & fast nature of the bulk & pack-
units we perform “novel” hybrid planning &
scheduling i.e., bulk-unit is scheduled & pack-unit is
planned to reduce solve time (“planuling”).
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Fast Moving Consumer Goods IMF
Bulk-Line
Sequence-Dependent
Switchovers
Forecasted & Firm
Future Demand Orders
Setup Times
Multiple Pack
Lines
Schedule slow units
(A model)
Plan fast units (A
model)
And then with separate
model schedule them (B
model)
Pack-Line
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Fast Moving Consumer Goods IMF
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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.
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Cogeneration (Steam/Power) IMF
Two multi-fuel steam boilers with three modes for
different operating regions and standby.
One steam turbogenerator to produce electrical
power from high-pressure steam.
One electrical power header with import & export
of power to plant.
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Cogeneration (Steam/Power) IMF
Fuel Header
Water Pump
Boiler1 w/ 3 Modes Boiler2 w/ 3 Modes
HP Steam Header
MP Steam Header
Power Header
Steam
Turbogenerator
Blowdown
Pressure Reducing
Valve
Multiple Modes
on Boilers
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Cogeneration (Steam/Power) IMF
2/11/2013 Copyright, Industrial Algorithms LLC
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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
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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.
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Power Generation IMF
Thermal & Hydro Plants
Three Buses/Nodes
1st & 2nd Kirchhoff Laws
Varying Demand Load
Voltage Phase Angles
Electrical
Engineering
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Capital Investment/Facilities Location IMF
Expansion?
Installation?
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Maritime Industrial Shipping IMF
Inventory Routing
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What is next?
Risk Management in the Supply-Chain
Supply-Chain Strategic Problem
Marketing Optimization
Wealth Management Optimization
On-Line Optimization (RTO)