Description
This problem finds a least cost shipping schedule that meets requirements at markets and supplies at factories. GAMS Connect is used to read and write CSV files. Note: In this example, the CSV data can also be read by using a simple table statement without doing further preprocessing.
Category : GAMS Data Utilities library
Main file : connect03.gms includes : connect03.gms
$title Simple Connect Example with CSV files (CONNECT03,SEQ=146)
$onText
This problem finds a least cost shipping schedule that meets
requirements at markets and supplies at factories.
GAMS Connect is used to read and write CSV files.
Note: In this example, the CSV data can also be read by using a simple
table statement without doing further preprocessing.
Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions.
Princeton University Press, Princeton, New Jersey, 1963.
This formulation is described in detail in:
Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide.
The Scientific Press, Redwood City, California, 1988.
The line numbers will not match those in the book because of these
comments.
Keywords: linear programming, transportation problem, scheduling
$offText
$onEcho > distance.csv
i,new-york,chicago,topeka
seattle,2.5,1.7,1.8
san-diego,2.5,1.8,1.4
$offEcho
$onEcho > capacity.csv
i,capacity
seattle,350
san-diego,600
$offEcho
$onEcho > demand.csv
j,demand
new-york,325
chicago,300
topeka,275
$offEcho
Set i 'canning plants', j 'markets';
Parameter d(i<,j<) 'distance in thousands of miles'
a(i) 'capacity of plant i in cases'
b(j) 'demand at market j in cases';
$onEmbeddedCode Connect:
- CSVReader:
file: distance.csv
name: d
indexColumns: 1
valueColumns: "2:lastCol"
- CSVReader:
file: capacity.csv
name: a
indexColumns: 1
valueColumns: 2
- CSVReader:
file: demand.csv
name: b
indexColumns: 1
valueColumns: 2
- GAMSWriter:
symbols: all
$offEmbeddedCode
Scalar f 'freight in dollars per case per thousand miles' / 90 /;
Parameter c(i,j) 'transport cost in thousands of dollars per case';
c(i,j) = f*d(i,j)/1000;
Variable
x(i,j) 'shipment quantities in cases'
z 'total transportation costs in thousands of dollars';
Positive Variable x;
Equation
cost 'define objective function'
supply(i) 'observe supply limit at plant i'
demand(j) 'satisfy demand at market j';
cost.. z =e= sum((i,j), c(i,j)*x(i,j));
supply(i).. sum(j, x(i,j)) =l= a(i);
demand(j).. sum(i, x(i,j)) =g= b(j);
Model transport / all /;
solve transport using lp minimizing z;
embeddedCode Connect:
- GAMSReader:
symbols:
- name: x
- Projection:
name: x.l(i,j)
newName: x_level(i,j)
- CSVWriter:
file: shipment_quantities.csv
name: x_level
unstack: True
endEmbeddedCode