version 1.5.1

merged iron python changes
fixed dippy (hopefully)

VERSION

@@ -1 +1 @@
-1.5.0
+1.5.1

examples/BeerDistributionProblem_resolve.py

@@ -0,0 +1,82 @@
+"""
+The Beer Distribution Problem for the PuLP Modeller
+Illustrates changing a constraint and solving
+
+Authors: Antony Phillips, Dr Stuart Mitchell  2007
+"""
+
+# Import PuLP modeler functions
+from pulp import *
+
+# 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 = makeDict([Warehouses,Bars],costs,0)
+
+# Creates the 'prob' variable to contain the problem data
+prob = LpProblem("Beer Distribution Problem",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 'Vars' is created to contain the referenced variables(the routes)
+vars = LpVariable.dicts("Route",(Warehouses,Bars),0,None,LpInteger)
+
+# The objective function is added to 'prob' first
+prob += lpSum([vars[w][b]*costs[w][b] for (w,b) in Routes]), "Sum_of_Transporting_Costs"
+
+# The supply maximum constraints are added to prob for each supply node (warehouse)
+for w in Warehouses:
+    prob += lpSum([vars[w][b] for b in Bars])<=supply[w], "Sum_of_Products_out_of_Warehouse_%s"%w
+
+# The demand minimum constraints are added to prob for each demand node (bar)
+# These constraints are stored for resolve later
+bar_demand_constraint = {}
+for b in Bars:
+    constraint = lpSum([vars[w][b] for w in Warehouses])>=demand[b]
+    prob += constraint, "Sum_of_Products_into_Bar_%s"%b
+    bar_demand_constraint[b] = constraint
+
+# The problem data is written to an .lp file
+prob.writeLP("BeerDistributionProblem.lp")
+for demand in range(500, 601, 10):
+    # reoptimise the problem by increasing demand at bar '1'
+    # note the constant is stored as the LHS constant not the RHS of the constraint
+    bar_demand_constraint['1'].constant = - demand
+    #or alternatively,
+    #prob.constraints["Sum_of_Products_into_Bar_1"].constant = - demand
+
+    # The problem is solved using PuLP's choice of Solver
+    prob.solve()
+
+    # The status of the solution is printed to the screen
+    print "Status:", 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 = ", value(prob.objective)

src/pulp/pulp.py

@@ -1,6 +1,6 @@
 #! /usr/bin/env python
 # PuLP : Python LP Modeler
-# Version 1.4.8
+# Version 1.5.1
 
 # Copyright (c) 2002-2005, Jean-Sebastien Roy (js@jeannot.org)
 # Modifications Copyright (c) 2007- Stuart Anthony Mitchell (s.mitchell@auckland.ac.nz)
@@ -152,14 +152,14 @@ class LpElement(object):
     """
     #to remove illegal characters from the names
     trans = string.maketrans("-+[] ->/","________")
-    def setname(self,name):
+    def setName(self,name):
         if name:
             self.__name = str(name).translate(self.trans)
         else:
             self.__name = None
-    def getname(self):
+    def getName(self):
         return self.__name
-    name = property(fget = getname,fset = setname)
+    name = property(fget = getName,fset = setName)
 
     def __init__(self, name):
         self.name = name
@@ -351,9 +351,6 @@ class LpVariable(LpElement):
         return d
     dict = classmethod(dict)
 
-    def getName(self):
-        return self.name
-
     def getLb(self):
         return self.lowBound
 
@@ -830,9 +827,6 @@ class LpConstraint(LpAffineExpression):
         self.sense = sense
         self.modified = True
 
-    def getName(self):
-        return self.name
-
     def getLb(self):
         if ( (self.sense == LpConstraintGE) or
              (self.sense == LpConstraintEQ) ):