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# A TRANSPORTATION PROBLEM
#
# This problem finds a least cost shipping schedule that meets
# requirements at markets and supplies at factories.
#
# References:
# Dantzig G B, "Linear Programming and Extensions."
# Princeton University Press, Princeton, New Jersey, 1963,
# Chapter 3-3.
set I;
/* canning plants */
set J;
/* markets */
set K dimen 2;
/* transportation lane */
set L;
/* parameters */
param a{i in I};
/* capacity of plant i in cases */
param b{j in J};
/* demand at market j in cases */
param d{i in I, j in J};
/* distance in thousands of miles */
param e{l in L};
/* parameters */
param f;
/* freight in dollars per case per thousand miles */
table tab_plant IN "CSV" "plants.csv" :
I <- [plant], a ~ capacity;
table tab_market IN "CSV" "markets.csv" :
J <- [market], b ~ demand;
table tab_distance IN "CSV" "distances.csv" :
K <- [plant, market], d ~ distance;
table tab_parameter IN "CSV" "parameters.csv" :
L <- [parameter], e ~ value ;
param c{i in I, j in J} := e['transport cost'] * d[i,j] / 1000;
/* transport cost in thousands of dollars per case */
var x{(i,j) in K} >= 0;
/* shipment quantities in cases */
minimize cost: sum{(i,j) in K} c[i,j] * x[i,j];
/* total transportation costs in thousands of dollars */
s.t. supply{i in I}: sum{(i,j) in K} x[i,j] <= a[i];
/* observe supply limit at plant i */
s.t. demand{j in J}: sum{(i,j) in K} x[i,j] >= b[j];
/* satisfy demand at market j */
solve;
table tab_result{(i,j) in K} OUT "CSV" "result.csv" :
i ~ plant, j ~ market, x[i,j] ~ shipment;
end;