the role and impact of algebraic modeling languages in and ... · impact of algebraic modeling...
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GAMS Development Corp. GAMS Software GmbH www.gams.com
The Role and Impact of Algebraic Modeling Languages
in and on Industrial Optimization Michael R. Bussieck: [email protected]
Franz Nelissen: [email protected] Lutz Westermann: [email protected]
Content
ALl
Head
Bad Honnef, November 2014
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GENERAL ALGEBRAIC MODELING SYSTEM
2
Company Background
Roots: World Bank, 1976
GAMS Development Corporation (Washington)
Tool Provider: General Algebraic Modeling System
Went commercial in 1987
GAMS Software GmbH (Cologne, Braunschweig)
1996
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GENERAL ALGEBRAIC MODELING SYSTEM
3
Agenda
Algebraic Modeling Languages – A Success Story
GAMS – Design Principles
Some Examples
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GENERAL ALGEBRAIC MODELING SYSTEM
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1976 - A World Bank Slide
The
Vision
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GENERAL ALGEBRAIC MODELING SYSTEM
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Algebraic Modeling Languages (AML)
1
• High-level computer programming languages
• Formulation of mathematical optimization problems
• Notation similar to algebraic notation
2 • Do not solve problems directly, but offer links to state-of-
the-art algorithms (“solver-links”)
Source: http://en.wikipedia.org/wiki/Algebraic_modeling_language
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GENERAL ALGEBRAIC MODELING SYSTEM
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2012 INFORMS Impact Prize
Originators of Algebraic Modeling Languages
36 Years
later
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GENERAL ALGEBRAIC MODELING SYSTEM
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Impact of Algebraic Modeling Languages
Tremendous increased productivity, quality, reliability and maintainability
Simplified model development, changes, and transfer
Made a scarce resource (good modelers) more productive
Why not use current big data systems
Impact of AML
1
2
Important vehicle to make mathematical optimization available to a broader audience
3
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GENERAL ALGEBRAIC MODELING SYSTEM
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Agenda
Algebraic Modeling Languages – A Success Story
GAMS – Design Principles
Some Examples
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GENERAL ALGEBRAIC MODELING SYSTEM
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Design Principles
Model
Platform Solver Data Interface
1 Simple modeling language with a balanced mix of declarative and procedural elements
2 Open architecture and interfaces to other systems
3 Independent layers
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1 • Language similar to mathematical notation
2 • Few basic language elements: sets, parameters,
variables, equations, models Easy to learn
3 • Lot‘s of code optimization under the hood
Simple Declarative Language
Model
Platform Solver Data Interface
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GENERAL ALGEBRAIC MODELING SYSTEM
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Mix of Declarative and Procedural Elements
Procedural elements like loops, for, if, macros and functions
Allow to build complex problem algorithms within GAMS
Interaction with other systems: • Job control • Data exchange
Model
Platform Solver Data Interface
Combine models inside the system
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Independence of Model and Operating System
Platforms supported by GAMS:
Model
Platform Solver Data Interface
Models can be moved between platforms with ease
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GENERAL ALGEBRAIC MODELING SYSTEM
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Independence of Model and Solver
All major commercial LP/MIP solver
Open Source Solver (COIN) Also solver for NLP, MINLP,
global, and stochastic optimization
One environment for a wide range of model types and solvers
Switching between solvers with one line of code
Model
Platform Solver Data Interface
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GENERAL ALGEBRAIC MODELING SYSTEM
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Independence of Model and Data
Application GAMS ASCII
• ASCII: Initial model development
• GDX: Binary Data layer (“contract”) between GAMS and applications – Platform independent – Direct GDX interfaces and
general API
Model
Platform Solver Data Interface
Application
GAMS
SOLVER
GDX
• Declarative Modeling: x(j), j∈{1,…}
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GENERAL ALGEBRAIC MODELING SYSTEM
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Independence of Model and User Interface
Model
Platform Solver Data Interface
1
• Open architecture and interfaces to other systems No preference for a particular user interface
2 • Application Programming Interfaces
• Low Level
• Object Oriented: .Net, Java, Python, …
3 • Smart Links to popular environments
• Excel, MATLAB, R, ...
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GENERAL ALGEBRAIC MODELING SYSTEM
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Broad Range of Application Areas
25+ Years GAMS Development
Agricultural Economics Applied General Equilibrium
Chemical Engineering Economic Development
Econometrics Energy
Environmental Economics Engineering
Finance Forestry
International Trade Logistics
Macro Economics Military
Management Science/OR Mathematics
Micro Economics Physics
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GENERAL ALGEBRAIC MODELING SYSTEM
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Broad User Community and Network
GAMS used in more than 120 countries
25+ Years GAMS Development
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GENERAL ALGEBRAIC MODELING SYSTEM
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Broad User Community and Network
More than 10,000 licenses
50% academic users, 50% commercial users
6,000+ monthly downloads of the free system
25+ Years GAMS Development
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GENERAL ALGEBRAIC MODELING SYSTEM
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Agenda
Algebraic Modeling Languages – A Success Story
GAMS – Design Principles
Some Examples
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“… - asking yourself why you would do optimization in GAMS, software that looks as modern and friendly as Richard Nixon.”
Customer review on Amazon.com for S. Zenios, S. Nielson, and A. Consiglio “Practical Financial Optimization: A Library of GAMS Models”, Wiley, 2009
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GENERAL ALGEBRAIC MODELING SYSTEM
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Three (Short) Examples
1
General Dynamics – retooling the solution engines for the generation and management of efficient frontiers of multi-objective design optimization problems
2 xyz Energy Company – solving many (500+) difficult MIP models in parallel on the cloud (security issues)
3 Class Scheduling at USMA West Point – student centric scheduling of classes
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GENERAL ALGEBRAIC MODELING SYSTEM
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General Dynamics Land Systems
ACSOM
• Advanced Collaborative System Optimization Modeler
• Explore high-dimensional Pareto surface for configuration of (expensive) military vehicles
• Collaboration between: • General Dynamics Land
Systems • Industrial & Systems
Engineering, Wayne State University (WSU)
• GAMS Development Corp • Amsterdam Optimization
Group
Abrams M1A2
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GENERAL ALGEBRAIC MODELING SYSTEM
Content
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GENERAL ALGEBRAIC MODELING SYSTEM
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GENERAL ALGEBRAIC MODELING SYSTEM
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Compromise Point alias(thisobj,obj); loop(thisobj, w(obj) = 0; w(thisobj) = 1; solve mod1 using mip max ztotal; f_best(thisobj) = z.l(thisobj); solve mod1 using mip min ztotal; f_worst(thisobj) = z.l(thisobj); f_range(thisobj) = f_best(thisobj) - f_worst(thisobj) ; );
equation distance 'distance to Utopia point'; variable sqdist 'minimum distance (normalized)'; distance.. sqdist =e= sum(obj, sqr((f_best(obj)-z(obj))/f_range(obj))); model mindist /all/; solve mindist minimizing sqdist using miqcp;
Utopia Point
How to calculate Efficient Frontier ?
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GENERAL ALGEBRAIC MODELING SYSTEM
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ACSOM Algorithm • Complete Enumeration • Evolutionary Algorithms • WSU Algorithms:
ACE, UPSA • Integer Cut
• GAMS/CPLEX solution pool to collect 1,000,000+ solutions of a small MIQP
• Compact representation of solutions
• Uses an algorithm to filter out dominated solution • implemented in traditional
programming language with efficient data structures
Many Algorithms
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GENERAL ALGEBRAIC MODELING SYSTEM
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Retooling of an Application
• VB.Net • MPL (single point) • CPLEX • SQL throughout
• GAMS (complete algorithm; streamlined design)
• GAMS .NET API for recursive ACE algorithm
• CPLEX • Bulk SQL just once
Old System New System
• On the same hardware: • From hours and even days for large problems • To minutes, up to an hour
Performance Improvements
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xyz Energy Company
Massive Parallel for Large Scale MIP
• Sensitivity Analysis subject to few parameter changes
• Failures with Stochastic Optimization and Robust Optimization
• Scenario Analysis works! • Task: Solve 1000 scenarios (MIPs, 1-2 hours)
every week overnight • Post processing (distribution of KPI) of results
outside modeling system
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GENERAL ALGEBRAIC MODELING SYSTEM
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Solving Independent Models
• Small Ratio of MP solution time/AML overhead
• GUSS/Scenario Solver • Large ratio i.e. only MP time is relevant (pre/post
processing not critical) • Grid Facility, Gurobi/Cplex Server
• If entire model run including pre processing/MP solve/post processing is costly • Parallel/asynchronous execution of entire model in
the cloud • Security issues in the cloud
• Obfuscation
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GENERAL ALGEBRAIC MODELING SYSTEM
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Massively Parallel MIP
• MIP/B&C Algorithm ideal to parallelize • Master/Worker Paradigm (process nodes in parallel)
• Software: FATCOP/Condor, BCP/PVM, PICO/MPI • Distributed MIP (sub-MIPs in parallel)
• Software: ParaSCIP, Cplex (12.x), Gurobi (6.0) • A-priori subdivision into n independent problems
• Seymour problem solved that way • Open Pit Mining (openpit in GAMS Model library)
• Partitioning integer variables to subdivide model into into 4096 sub-problems
• Experiments (Ferris) at UW using Condor Pool
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HTCondor (previously Condor)
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GENERAL ALGEBRAIC MODELING SYSTEM
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Results for 4096 MIPS on HTCondor
• Submission started Jan 11,16:00 • All jobs submitted by Jan 11, 23:00 • All jobs returned by Jan 12, 12:40
• 20 hours wall time, 5000 CPU hours • Peak number of CPU’s: 500
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AWS Cloud Setup at xyz Company
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Configure the Worker Instance
• Standard EC2 Linux image
• Prepare mounting the S3 bucket on instance start up
• Install GAMS • Starting the Python xmlrpc server on start up • Add the following lines to /etc/rc.d/rc.local
mount the S3 bucket
start the daemon with the RCP server
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RPC Server – Run at Machine Start Up
• Start of server
• Asynchronous run of GAMS Job
• GAMS OO API to control GAMS Job
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Submitter - Managing EC2 instances
• Start instances
• Check status
• Terminate instances
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Submitter - Communicating via RPC
• Establish connection
• Call functions provides by the RPC server • Start job on server • Check status and collect results
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GENERAL ALGEBRAIC MODELING SYSTEM
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Protecting IP and Sensitive Data
• Obfuscate or hide sensitive information • Extrinsic function libraries • External Equations • Secure work files • Encrypted source files • Obfuscated files • change all the names and other
documentation related to a specific model run
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Obfuscated Model - Example
• GAMS Model Library: indus89 + scenario data
• Create obfuscated save file (compile only) • gams indus89 action=compile so=indus89A
• Send to cloud and execute • gams runscn –scennumber=n r=indus89A s=n
• Copy save files 1,…,n back and create named results • gams result –scennumber=n r=n rn=indus89
• Merge results (e.g. gdxmerge) • gdxmerge *_result.gdx
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Obfuscated Original
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Class Scheduling at USMA • 1999:
• Class Scheduling at United States Military Academy, West Point, NY, USA
• Soon after Term End Exam Scheduling (TEE) at USMA
“ … each student’s daily activities are a carefully regimented balance of academic, military, and physical requirements.”
• 2014: • Applications are
alife and kicking • New UI &
computing platform
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USMA is Different
• Technically • Day1/day2 schedule • Special rules (e.g. < 30% athletes in class)
• Scheduling around the cadets needs • No conflicting activities • Individual schedule of activities is compliant
to vast catalogue of business rules
• Proper planning
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ATTENDANCE PERIODS
OFFICE OF THE DEAN UNITED STATES MILITARY ACADEMY
AUGUST 1999 (EDITION OF AUG 98 IS OBSOLETE)
2-DAY CLASS PERIODS
LAB
K L NOON MEAL
DEAN’S HOUR R/S/T/U LAB HOURS
LAB
I LAB
LAB G H J
1-DAY CLASS PERIODS 0735 1550 0945 1245 1340 1445 0830 0840 0935 1040 1050 1145 1350 1455
LAB
C D
LAB
E F NOON MEAL
CMDT’S HOUR
LAB
A B LAB
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27
SEPTEMBER 1999
13
HOME FOOTBALL
BALL STATE
2-13 1-13 2-14
1-14
2-15
A/D
1-15 2-16 1-16
LABOR DAY
(No Classes) R 1-8 2-9 1-9 2-10
S
A/D
T U 1-10
2-11 1-11 2-12 1-12
A/C
R
AWAY FOOTBALL
TULANE
T
HOME FOOTBALL
WAKE FOREST
S
1-6
2-7 1-7
2-8 B
U
1 2 3 4
5 6 7 8 9 10 11
12 14 15 16 17 18
19 20 21 22 23 24 25
26 28 29 30
AUGUST 1999
DECEMBER 1999 JANUARY 2000
MARCH 2000 APRIL 2000 MAY 2000 JUNE 2000
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
13
6
20
27
STAP
ENDS STAP
GRADUATION
RECEPTION
“R” DAY
1 2 3
4 5 7 8 9 10
11 12 14
15 16 17
18 21 22 23 24
25 26 28 29 30
19
FEBRUARY 2000
A/C 1 2 3 4 5
6 7
14
21
28
B for 1st Class 100TH NIGHT
2-13 1-13
2-14
1-14
2-15
A/D
R S T B 1-10 2-11 1-11 2-
12
1-6 2-7
1-7
1-5
2-6 T U
1-8 2-9 1-9 2-10 2-8
PRESIDENTS DAY
(No Classes)
U R
S
T
13
8 9 10 11 12
15 16 17 18 19
20
22 23 24 25 26
27
1-12
RMC @ RMC
OCTOBER 1999
11
4
18
25
HOME FOOTBALL LOUISVILLE
PLEBE PARENT
WEEK END ( NO CLASSES)
1-22 2-23
1-23
2-24
1-24
R S
U 2-20 2-21 1-21 2-
22
B
R HOMECOMING FOOTBALL
NEW MEXICO
1-18 2-19 1-19
2-18
COLUMBUS DAY
(No Classes)
T U R 2-25
1-25 2-26 1-26 2-27 A/D
HOME FOOTBALL
E CAROLINA
2-17
1-17 A/D
U
1 2
3 5 6 7 8 9
10 12 13 14 15 16
17 19 20 21 22 23
24 26 27 28 29 30
31
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
A/D AWAY
FOOTBALL MEMPHIS
HOME FOOTBALL HOUSTON
THANKSGIVING LEAVE
B AWAY
FOOTBALL AIR FORCE
R
2-32 1-32 2-33 1-33 2-34
U
A/C
T S 2-29
1-29 1-27 2-28 1-28
U
VETERANS DAY
(No Classes)
2-31 1-31 2-30 1-30
S T
1-34 2-35 1-35 COMPRESSED
SCHEDULE
5 6
7
1 2 3 4
26 27
28
25 22 23 24
29 30
12
14
13 11 8 9 10
19 20
21
18 15 16 17
1-36 THANKS- GIVING
LEAVE ENDS
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY B 2 3 4
5
1 SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY
SATURDAY
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
WINTER LEAVE
A/C YEARLING WEEKEND
1
2 3 4 5 6 7 8
9 10 11 12 13 14 15
16
17 18 19 20 21 22
23 24 25 26 27 28 29
30 31
WINTER LEAVE ENDS MILITARY INTERSESSION
MILITARY INTERSESSION
M.L KING’S B’DAY
(No Classes)
B
500TH NIGHT
2-1
1-1 2-2 1-2
2nd Term Begins
2-3 1-3 2-4 1-4 2-5 R S
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY
SATURDAY A/
C
SPRING
LEAVE
BEGINS
A/C
2-21 1-21
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U
S T
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S
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CLASS DAY
2-19 1-19 2-20
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SPRING LEAVE
21 22 23 24 25
26 27 28 29 30 31
1 2 3 4
5 6 7 8 9 10 11
12 13
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SPRING
LEAVE
ENDS
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY
SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY
SATURDAY
9
2
16
23
30
NOVEMBER 1999
LEGEND INDICATES A or B SATURDAY
R/S/T/U FIXED LAB SCHEDULED IN DEANS HOUR /K HOUR
A 2-12 R
1 DATE
1-DAY or 2-DAY
LESSON NUMBER FOR 40 LESSON COURSES
1-DAY/2-DAY SCHEDULE: AY 99/00
FOOTBALL
BEAT NAVY!
2-36
1-37 2-37
1-38 S
U
R
T 2-40 2-
38 1-39 2-39 1-40 TERM END
EXAMS BEGIN
TERM END EXAMINATIONS
WINTER LEAVE
WINTER LEAVE
6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 31
WINTER LEAVE BEGINS
B
A/D
2-31 1-31
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1-32
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1-25
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S
A/C
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R
U EASTER
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2-28
2-26 R
U FEE
S
R
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1
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30
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A/C
RING WEEKEND
1
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8
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1-3
2-4 1-4 2-5
1-5
1st Term Begins
R S
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B 17 18
19 20 21
24 25 26 27 28
29 2-6 T
31
2-1 1-1 2-2 1-2 2-3
REORGANIZATION WEEK
S
GRADUATION
DAY
S T
U
1-38
2-39 1-39 2-40 1-40
A/D
1-36 2-37
1-37
2-38
2-36
TERM END EXAMINATIONS
31 30
1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19
20
21 22
23 24 25 26 27
28 29 MEMORIAL
DAY CLASSES
IN SESSION
STAP BEGINS
AWAY FOOTBALL S.MISSISSIPPI
29
A/D
CLASS DAY
WPPWE 1930
A/C
SANDHURST
T 1-20
COMPRESSED
SCHEDULE
A/C
WPPWE 1930
Make ahead WPPWE
Make ahead WPPWE
Class Day
Last Updated: 5 May 1999
PLEBE PARENT WEEKEND
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GENERAL ALGEBRAIC MODELING SYSTEM
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USMA is Different
• Technically • Day1/day2 schedule • Special rules (e.g. < 30% athletes in class)
• Scheduling around the cadets needs • No conflicting activities • Individual schedule of activities is compliant
to vast catalogue of business rules
• Proper planning
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Academic Program
• 8 Term Academic Program (8TAP) • For example, Mathematics Major:
Statistics/Optimization
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GENERAL ALGEBRAIC MODELING SYSTEM
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Enterprise Academy Management System
• Management of :
• Redbook • Registration • Schedule • Facility • Attendance • Grades
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Term End Exam (TEE) • Find good
schedules for exam courses and cadets
Utopia Point
Academic Scheduling
Course Scheduling • For a given set of course
offerings find good schedules for all cadets.
• Scheduling preps • Term balancing
(department resources) • Initial course offerings • Scheduling • Manual post scheduling
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Tight
Depts
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Scheduling Cadet Courses to Hours
Given course hours & capacity MA481,AB,36 MA481,CD,18 MA481,EF,18
PE300,C,180 PE300,J,60 MA371,F,18
Given cadet’s course registration 043671571,MA481
043671571,PE300
Objective: Find a good assignment of cadet’s course requests to course hours 043671571,MA481,CD
043671571,PE300,J
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Scheduling System Objectives
• Produced in a timely fashion • Complies with most constraints/rules…
• Individual: • Cadet can’t be at two places at the same time • Course hours are balanced over Day-1, Day-2 • Free hour • Cadet gets scheduled for all courses he/she has
registered for
• Capacity: • Enrollment of course hour cannot exceed capability • Course enrollment of freshmen which are also
athletes cannot exceed 60% of the total enrollment
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GENERAL ALGEBRAIC MODELING SYSTEM
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Legacy Scheduling System
• Matching algorithm based on cadet’s
course registration only • Reporting programs check schedule
against all constraints and business rules • Last years (1998): 80% of cadets
rescheduled manually • Time to generate schedule: 4 weeks (3
Schedulers)
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An Optimization Model
• 60,000 Variables, 500,000 Non-Zeros • 24 hours CPLEX 6.6 and no integer solution
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Problems with a MP Model
• There is no solution subject to all
constraints/rules for real data
• What is the objective for the model? • Goal Programming: • Relax constraints/rules by penalizing
violations. Select penalties for violations: Calibration of the model.
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GENERAL ALGEBRAIC MODELING SYSTEM
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A 2-Stage Approach
• Prescheduling • Filter cadets with no feasible schedule • Overcome infeasibility by relaxation/data
changes • Scheduling • All individual constraints/rules are hard
constraints • Find assignment that does not exceed
capacity (or penalize overloads)
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GENERAL ALGEBRAIC MODELING SYSTEM
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One cadet at a time • Check feasibility • If infeasible produce
several infeasible schedules ranked by severity of infeasibility • Hour Conflict • Day – Day Balance • Last Hour Free
• Human intervenes
Prescheduling
All Cadets
feasible
infeasible
Relaxation
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GENERAL ALGEBRAIC MODELING SYSTEM
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Scheduling in Batches
• Now individual constraints allow
feasible schedules for all cadets (hard constraints)
• Tight Capacity of Course Hours may still result in an infeasible model:
Assign Overload Penalties • Model with all cadets still unsolvable!
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GENERAL ALGEBRAIC MODELING SYSTEM
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Scheduling
• Take a batch of cadets (10-30) and schedule them independent of the others
• Objective: Meet the capacity profile of the course hours (Penalize Over- and Underload)
• Example: • 3 course hours MA481 AB (20), CD,IJ (10) • Batch contains 8 cadets with request for MA481
AB
CD EF 4 2 2
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GENERAL ALGEBRAIC MODELING SYSTEM
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Results
• AY 2000/2 tested in parallel • AY 2001/1 deployed
Legacy System New System
Individual Relaxations
free/group/unbalanced
203/304/116 58/25/4
Capacity Overloads
Courses/seats
12/54 9/21
Number of Schedulers 3 1
Time to produce Schedule
4 Weeks 1 Day
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Summary/Some Lessons Learned
• Know what you can add to a project
(modeling, optimization, algorithms) • For successful application know more
than math/modeling or work with competent people that know their stuff
• Use as much off-the-shelf products AML, Excel, Matlab, R, …
• Visualization of results
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EMS-EDM Prophet
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EMS-EDM PROPHET® – Modules 2015
© 2014 Fraunhofer IOSB-AST
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EMS-EDM PROPHET® – Anwender
© 2014 Fraunhofer IOSB-AST
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EMS-EDM PROPHET® – Übersicht
© 2014 Fraunhofer IOSB-AST
Mathematical Optimization
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EMS-EDM PROPHET–RPS
© 2014 Fraunhofer IOSB-AST
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EMS-EDM PROPHET – RPS
© 2014 Fraunhofer IOSB-AST
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RPS – Modeling
© 2014 Fraunhofer IOSB-AST
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RPS - Modeling
© 2014 Fraunhofer IOSB-AST
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RPS – GAMS Log Lade Optimierungs-DLL Operation abgeschlossen 00:00 Lade Zeitbasis Operation abgeschlossen: 00:00 Erzeuge Porjektverzeichnis und entferne temporäre Dateien Operation abgeschlossen: 00:00 Erzeuge GAMS-Datei ... schreibe Komponentendeklarationen schreibe GDX-Daten schreibe Instanzwerte schreibe Set-Datenwerte schreibe Set-Datenwerte für die Startbedingungen schreibe Tabellen schreibe Tabelle "Startzustandstabelle [NW_BK_034] " ... Zeitbedarf GDX-Datei-Erzeugung [hh:mm:ss]: 00:00:09 schreibe Modelldaten schreibe Instanzgleichungen schreibe Bilanzgleichungen schreibe Untermodellgleichungen schreibe Objektfunktion Schreiben der Deklarationsdatenwerte Schreiben der Deklarationswerte abgeschlossen Erzeuge Optionsdatei (OptFile) Zeitbedarf GAMS-Datei-Erzeugung [hh:mm:ss]: 00:02:34 .. Durchführung der Optimierung ... [19.03.2014 01:00 - 23.03.2014 00:00] Datei: "C:\GAMS\win64\24.3\gams.exe" Parameter: "C:\EMS-EDM PROPHET\system\Rps-
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EMS-EDM PROPHET–RPS
© 2014 Fraunhofer IOSB-AST
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Fields of Fuel
A Multiplayer, Web-based Simulation
Game
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Fields of Fuel (FoF) - Motivation
FoF allow classes to explore the complex sustainability challenges associated with growing bioenergy crops.
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Sustainable Bioenergy Crop Production
SUSTAINABILITY CATEGORY
SCORE COMPONENT SCORE SUBCOMPONENTS (DATA)
SOCIAL ENERGY PRODUCTION • CROP YIELD (MEGAGRAMS OF BIOMASS)
• NET ENERGY (MEGAJOULES)
ECONOMIC INCOME • NET INCOME ($)
ENVIRONMENTAL ENVIRONMENTAL QUALITY • SOIL FERTILITY (SOIL CARBON) • WATER QUALITY (INDEX) • BENEFICIAL BUGS (INDEX) • GREENHOUSE GAS EMSSIONS (CO2
EQ.)
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Sustainable Bioenergy Crop Production
Crop Choices Economics Energy/Yield Environment
Corn High High Low
Alfalfa Medium Low (N/A) High
Switchgrass Low Medium High
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Fields of Fuel – Setting up a Game
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Fields of Fuel – GAMS robots
• Single player have the option of choosing the number of bots to play against; the player can also set the objective of each bot opponent.
• The robots optimize a weighted average of overall score and field health over a five year period.
• A mixed-integer nonlinear programming problem (MINLP) identifies the optimal future path to follow.
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Fields of Fuel
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Fields of Fuel
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Fields of Fuel
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Fields of Fuel
fieldsoffuel.org
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Summary
AML – A Success Story
GAMS - Design Principles
• Simple, but powerful language for optimization problems
• Open interfaces
• Different layers
Examples
• Provide cutting edge technology
• Don’t lock developers and users into a certain environment
• Protect investments of users
• Make mathematical optimization available to a broader audience
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GAMS Development Corp. GAMS Software GmbH www.gams.com
Thank You USA
GAMS Development Corp. 1217 Potomac Street, NW Washington, DC 20007
USA Phone: +1 202 342 0180 Fax: +1 202 342 0181
Europe GAMS Software GmbH P.O. Box 40 59 50216 Frechen, Germany Phone: +49 221 949 9170 Fax: +49 221 949 9171 [email protected]
Bad Honnef, November 2014