diff --git a/src/pg/sql/25_optimization.sql b/src/pg/sql/25_optimization.sql
new file mode 100644
index 0000000..79a2110
--- /dev/null
+++ b/src/pg/sql/25_optimization.sql
@@ -0,0 +1,16 @@
+CREATE OR REPLACE FUNCTION 
+CDB_OptimAssignments(drain text,
+                     source text,
+                     drain_capacity text,
+                     source_production text,
+                     marginal_cost text)
+RETURNS setof int AS $$
+
+from crankshaft.optimization import Optim
+
+optim = Optim(drain, source, drain_capacity, source_production, marginal_cost)
+x = optim.optim()
+print(x)
+
+return x
+$$ LANGUAGE plpythonu;
diff --git a/src/py/crankshaft/crankshaft/optimization/__init__.py b/src/py/crankshaft/crankshaft/optimization/__init__.py
new file mode 100644
index 0000000..be46287
--- /dev/null
+++ b/src/py/crankshaft/crankshaft/optimization/__init__.py
@@ -0,0 +1 @@
+from optim import *
diff --git a/src/py/crankshaft/crankshaft/optimization/optim.py b/src/py/crankshaft/crankshaft/optimization/optim.py
new file mode 100644
index 0000000..ae9f94b
--- /dev/null
+++ b/src/py/crankshaft/crankshaft/optimization/optim.py
@@ -0,0 +1,114 @@
+"""optimization"""
+import sys
+import cvxopt
+from cvxopt.glpk import ilp
+import numpy as np
+import plpy
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+class Optim(object):
+    """Linear optimization class for logistics cost minimization"""
+
+    def __init__(self, drain_table, source_table, capacity_column,
+                 production_column, marginal_column, **kwargs):
+
+        # set data provider (defaults to SQL database access
+        self.data_provider = kwargs.get('data_provider',
+                                        AnalysisDataProvider())
+
+        # optional params
+        self.waste_per_person = kwargs.get('waste_per_person', 0.01)
+        self.recycle_rate = kwargs.get('recycle_rate', 0.0)
+        self.dist_cost = kwargs.get('dist_cost', 0.15)
+
+        # data sources
+        self.drain_table = drain_table
+        self.source_table = source_table
+
+        # model data
+        self.plant_capacity = self.data_provider.get_column(drain_table,
+                                                            capacity_column)
+        self.waste_in_area = (0.01 * (1. - self.recycle_rate) *
+                              self.data_provider.get_column(source_table,
+                                                            production_column))
+        self.marginal_cost = self.data_provider.get_column(drain_table,
+                                                           marginal_column)
+        # derivative data
+        self.distances = self.data_provider.get_pairwise_distances(source_table,
+                                                                   drain_table)
+        self.n_areas = len(self.waste_in_area)
+        self.n_plants = len(self.distances)
+        self.cost = self.calc_cost()
+
+    def test(self):
+        """
+        just plpy.notice the stored information
+        """
+
+        plpy.notice(self.source_table)
+        plpy.notice(self.drain_table)
+        plpy.notice(self.distances)
+        plpy.notice(self.plant_capacity)
+        plpy.notice(self.waste_in_area)
+        return None
+
+    def cost_func(self, distance, waste, marginal):
+        """
+        cost equation
+        """
+        return waste * (marginal + self.dist_cost * distance)
+
+    def calc_cost(self):
+        """
+        Populate an d x s matrix according to the cost equation
+
+        :returns: d x s matrix of costs from area i to plant j
+        :rtype: NumPy matrix
+        """
+        plpy.notice('self.waste_in_area: {}'.format(str(self.waste_in_area.shape)))
+        plpy.notice(self.waste_in_area)
+        plpy.notice('self.marginal_cost: {}'.format(str(self.marginal_cost.shape)))
+        plpy.notice(self.marginal_cost)
+        plpy.notice('self.distances: {}'.format(str(self.distances.shape)))
+        plpy.notice(self.distances)
+        costs = np.array([self.cost_func(distance,
+                                         self.waste_in_area[pair[1]],
+                                         self.marginal_cost[pair[0]])
+                          for pair, distance in np.ndenumerate(self.distances)])
+        return costs
+
+    def optim(self):
+        """solve linear optimization problem
+        Equations of the form:
+
+        minimize   c'*x
+        subject to G*x <= h
+                   A*x = b
+                   x[k] is binary
+        """
+        # costs
+        # elements chosen to minimize sum
+        c = cvxopt.matrix(self.cost.ravel('F'))
+
+        # equality constraint variables
+        # each area is serviced once
+        A = cvxopt.spmatrix(1., [i // self.n_plants
+                                 for i in range(self.n_plants * self.n_areas)],
+                                range(self.n_plants * self.n_areas))
+        b = cvxopt.matrix(np.ones((self.n_areas, 1)), tc='d')
+
+        # inequality constraint variables
+        # each plant never goes over capacity
+        h = cvxopt.matrix(self.plant_capacity)
+        G = cvxopt.spmatrix(np.repeat(self.waste_in_area, self.n_plants),
+                            [i % self.n_plants
+                             for i in range(self.n_plants * self.n_areas)],
+                            range(self.n_plants * self.n_areas))
+
+        # solve
+        sol, x = ilp(c=c, G=G, h=h, A=A, b=b)
+        # assignment = np.array(x).reshape((self.n_areas, self.n_plants))
+        if sol != 'optimal':
+            raise Exception("Solution not soluble: {}".format(sol))
+
+        return np.array(x)