Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably

52 Pages·2011·0.52 MB·English

by  Robert Fourer

Using AMPL with Gurobi
to Apply Optimization Models
Efficiently and Reliably
Robert Fourer
AMPL Optimization
Industrial Engineering & Management Sciences,
Northwestern University
www.ampl.com — [email protected], [email protected]
Gurobi Optimizer Solution Seminar
Tokyo, 2 December 2011
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 1
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Outline
Motivation
 The optimization modeling cycle
 Optimization modeling languages
 AMPL and Gurobi
Introductory example
The AMPL company
AMPL’s users
 Commercial
 Government
 Research & teaching
Future directions
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 3
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
The Optimization Modeling Cycle
Steps
 Communicate with problem owner
 Build model
 Build datasets
 Generate optimization problems
 Feed problems to solvers (Gurobi)
 Solve
 Process results for analysis & reporting
 Repeat!
Goals
 Do this quickly and reliably
 Get results before client loses interest
 Deploy for application
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 4
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Optimization in 1980
“We do not feel that the linear programming user’s
most pressing need over the next few years is for a
new optimizer that runs twice as fast on a
machine that costs half as much (although this
will probably happen). Cost of optimization is just
not the dominant barrier to LP model
implementation. The process required to manage
the data, formulate and build the model, report on
and analyze the results costs far more, and is
much more of a barrier to effective use of LP, than
the cost/performance of the optimizer.”
Krabek, Sjoquist, Sommer,
“The APEX Systems: Past and Future.”
SIGMAP Bulletin 29 (April 1980) 3-23.
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 5
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Optimization Modeling Languages
Two forms of an optimization problem
 Modeler’s form
 Mathematical description, easy for people to work with
 Algorithm’s form
 Explicit data structure, easy for solvers to compute with
Idea of a modeling language
 A computer-readable modeler’s form
 You write optimization problems in a modeling language
 Computers translate to algorithm’s form for solution
Advantages of a modeling language
 Faster modeling cycles
 More reliable modeling and maintenance
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 6
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Algebraic Modeling Languages
Formulation concept
 Define data in terms of sets & parameters
 Analogous to database keys & records
 Define decision variables
 Minimize or maximize a function of decision variables
 Subject to equations or inequalities
that constrain the values of the variables
Advantages
 Familiar
 Powerful
 Implemented
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 7
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
The AMPL Modeling Language
Features
 Algebraic modeling language
 Variety of data sources
 Connections to all solver features
 Interactive and scripted control
Advantages
 Powerful, general expressions
 Natural, easy-to-learn design
 Efficient processing scales well with problem size
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 8
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
AMPL with Gurobi
Features
 Detection of all supported problem types
 Access to all algorithm & display options
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 9
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Introductory Example
Multicommodity transportation . . .
 Products available at factories
 Products needed at stores
 Plan shipments at lowest cost
. . . with practical restrictions
 Cost has fixed and variables parts
 Shipments cannot be too small
 Factories cannot serve too many stores
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 10
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011
Multicommodity Transportation
Given
(cid:1841) Set of origins (factories)
(cid:1830) Set of destinations (stores)
(cid:1842) Set of products
and
(cid:1853) Amount available, for each (cid:1861) ∈ (cid:1841) and (cid:1868) ∈ (cid:1842)
(cid:3036)(cid:3043)
(cid:1854) Amount required, for each (cid:1862) ∈ (cid:1830) and (cid:1868) ∈ (cid:1842)
(cid:3037)(cid:3043)
(cid:1864) Limit on total shipments, for each (cid:1861) ∈ (cid:1841) and (cid:1862) ∈ (cid:1830)
(cid:1861)(cid:1862)
(cid:1855) Shipping cost per unit, for each (cid:1861) ∈ (cid:1841), (cid:1862) ∈ (cid:1830), (cid:1868) ∈ (cid:1842)
(cid:1861)(cid:1862)(cid:1868)
(cid:1856) Fixed cost for shipping any amount from (cid:1861) ∈ (cid:1841) to (cid:1862) ∈ (cid:1830)
(cid:1861)(cid:1862)
(cid:1871) Minimum total size of any shipment
(cid:1866) Maximum number of destinations served by any origin
Robert Fourer, Using AMPL with Gurobi to Apply Optimization Models Efficiently and Reliably 11
Gurobi Optimizer Solution Seminar, Tokyo, 2 December 2011