# The cannery problem

A JuMP implementation of the cannery problem from:

Dantzig, Linear Programming and Extensions, Princeton University Press, Princeton, NJ, 1963.

It was originally contributed by Louis Luangkesorn, January 30, 2015.

```
using JuMP
import GLPK
function example_cannery()
# Origin plants.
plants = ["Seattle", "San-Diego"]
num_plants = length(plants)
# Destination markets.
markets = ["New-York", "Chicago", "Topeka"]
num_markets = length(markets)
# Capacity and demand in cases.
capacity = [350, 600]
demand = [300, 300, 300]
# Distance in thousand miles.
distance = [2.5 1.7 1.8; 2.5 1.8 1.4]
# Cost per case per thousand miles.
freight = 90
cannery = Model()
set_optimizer(cannery, GLPK.Optimizer)
# Create decision variables.
@variable(cannery, ship[1:num_plants, 1:num_markets] >= 0)
# Ship no more than plant capacity
@constraint(
cannery, capacity_con[i = 1:num_plants], sum(ship[i, :]) <= capacity[i]
)
# Ship at least market demand
@constraint(
cannery, demand_con[j = 1:num_markets], sum(ship[:, j]) >= demand[j]
)
# Minimize transporatation cost
@objective(
cannery,
Min,
sum(
distance[i, j] * freight * ship[i, j]
for i = 1:num_plants, j = 1:num_markets
)
)
optimize!(cannery)
println("RESULTS:")
for i = 1:num_plants
for j = 1:num_markets
println(" $(plants[i]) $(markets[j]) = $(value(ship[i, j]))")
end
end
return
end
example_cannery()
```

RESULTS: Seattle New-York = 50.0 Seattle Chicago = 300.0 Seattle Topeka = 0.0 San-Diego New-York = 250.0 San-Diego Chicago = 0.0 San-Diego Topeka = 300.0

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