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"""
The Beer Distribution Problem for the PuLP Modeller
Authors: Antony Phillips, Dr Stuart Mitchell 2007
"""
# Import PuLP modeler functions
import pulp
# Creates a list of all the supply nodes
warehouses = ["A", "B"]
# Creates a dictionary for the number of units of supply for each supply node
supply = {"A": 1000,
"B": 4000}
# Creates a list of all demand nodes
bars = ["1", "2", "3", "4", "5"]
# Creates a dictionary for the number of units of demand for each demand node
demand = {"1":500,
"2":900,
"3":1800,
"4":200,
"5":700,}
# Creates a list of costs of each transportation path
costs = [ #Bars
#1 2 3 4 5
[2,4,5,2,1],#A Warehouses
[3,1,3,2,3] #B
]
# The cost data is made into a dictionary
costs = pulp.makeDict([warehouses, bars], costs,0)
# Creates the 'prob' variable to contain the problem data
prob = pulp.LpProblem("Beer Distribution Problem", pulp.LpMinimize)
# Creates a list of tuples containing all the possible routes for transport
routes = [(w,b) for w in warehouses for b in bars]
# A dictionary called x is created to contain quantity shipped on the routes
x = pulp.LpVariable.dicts("route", (warehouses, bars),
lowBound = 0
cat = pulp.LpInteger)
# The objective function is added to 'prob' first
prob += sum([x[w][b]*costs[w][b] for (w,b) in routes]), \
"Sum_of_Transporting_Costs"
# Supply maximum constraints are added to prob for each supply node (warehouse)
for w in warehouses:
prob += sum([x[w][b] for b in bars]) <= supply[w], \
"Sum_of_Products_out_of_Warehouse_%s"%w
# Demand minimum constraints are added to prob for each demand node (bar)
for b in bars:
prob += sum([x[w][b] for w in warehouses]) >= demand[b], \
"Sum_of_Products_into_Bar%s"%b
# The problem data is written to an .lp file
prob.writeLP("BeerDistributionProblem.lp")
# The problem is solved using PuLP's choice of Solver
prob.solve()
# The status of the solution is printed to the screen
print "Status:", pulp.LpStatus[prob.status]
# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
print v.name, "=", v.varValue
# The optimised objective function value is printed to the screen
print "Total Cost of Transportation = ", prob.objective.value()
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