Added the linear program solving the midatlantic region.

diff --git a/bin/linear_programs/midatl b/bin/linear_programs/midatl
new file mode 100644 (file)
index 0000000..246e34b
--- /dev/null
+++ b/bin/linear_programs/midatl
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+
+"""
+Solve an example problem with lp_solve.
+"""
+
+from math import acos, sin, cos
+import os
+import site
+import sys
+
+# Basically, add '../src' to our path.
+# Needed for the imports that follow.
+site.addsitedir(os.path.dirname(os.path.abspath(sys.argv[0])) + '/../../src')
+
+import LinearProgramming
+
+
+class Facility(object):
+
+    def __init__(self, fac_id, fac_capacity, fac_latitude, fac_longitude):
+        self.id = fac_id
+        self.capacity = fac_capacity
+        self.latitude = fac_latitude
+        self.longitude = fac_longitude
+        
+    def distance(self, other_facility):
+        return 3963.0 * acos(sin(self.latitude/57.2958) * sin(other_facility.latitude/57.2958) + cos(self.latitude/57.2958) * cos(other_facility.latitude/57.2958) *  cos(other_facility.longitude/57.2958 - self.longitude/57.2958))
+
+
+class Consumer(Facility):
+    def __init__(self, fac_id, fac_capacity, fac_latitude, fac_longitude):
+        super(Consumer, self).__init__(fac_id,
+                                       fac_capacity,
+                                       fac_latitude,
+                                       fac_longitude)
+
+class Producer(Facility):
+    def __init__(self, fac_id, fac_capacity, fac_latitude, fac_longitude):
+        super(Producer, self).__init__(fac_id,
+                                       fac_capacity,
+                                       fac_latitude,
+                                       fac_longitude)
+
+f = open('midatl.csv', 'r')
+f.readline() # Skip the header
+
+producers = []
+consumers = []
+
+for line in f:
+    row = line.split(',')
+    fac_id = int(row[0])
+    fac_type = row[1]
+    fac_capacity = float(row[2])
+    fac_latitude = float(row[3])
+    fac_longitude = float(row[4])
+    
+    if fac_type == '"D"':
+        c = Consumer(fac_id, fac_capacity, fac_latitude, fac_longitude)
+        consumers.append(c)
+    else:
+        p = Producer(fac_id, fac_capacity, fac_latitude, fac_longitude)
+        producers.append(p)
+
+
+
+lp = LinearProgramming.LinearProgram()
+
+# Loop through the consumer/producer pairs, calculating costs
+# (distances) and adding them to the array of objective function
+# coefficients.
+lp.objective_coefficients = []
+
+for c_idx in range(0, len(consumers)):
+    for p_idx in range(0, len(producers)):
+        distance = consumers[c_idx].distance(producers[p_idx])
+        lp.objective_coefficients.append(distance)
+
+
+num_cols = len(lp.objective_coefficients)
+
+lp.constraint_matrix = []
+lp.inequalities = []
+lp.rhs = []
+
+for c_idx in range(0, len(consumers)):
+    demand = consumers[c_idx].capacity
+    lp.rhs.append(demand)
+    
+    lp.inequalities.append(LinearProgramming.GE)
+
+    demand_row = [0]*num_cols
+    offset = c_idx * len(producers)
+    for idx in range(0, len(producers)):
+        demand_row[offset+idx] = 1
+
+    lp.constraint_matrix.append(demand_row)
+
+for p_idx in range(0, len(producers)):
+    supply = producers[p_idx].capacity
+    lp.rhs.append(supply)
+
+    lp.inequalities.append(LinearProgramming.LE)
+    
+    supply_row = [0]*num_cols
+    
+    for idx in range(0,len(consumers)):
+        offset = p_idx + len(producers)*idx
+        supply_row[offset] = 1
+    lp.constraint_matrix.append(supply_row)
+
+lp.type = LinearProgramming.MINIMIZE
+
+[v,x,duals] = lp.solve()
+
+print 'Optimal objective function value: ', v
+print 'Optimal solution vector: ', x