# Linear Programming¶

Linear programming is the minimization (or maximization) of a linear objective subject to linear constraints. There are several widely adopted schemes for representing linear programming problems. Here we adopt a scheme corresponding where the linear objective is written in terms of decision variables $x_1, x_2, \ldots, x_N$ as

\begin{align} \min_{x_1, x_1, \ldots, x_N} c_1x_1 + c_2x_2 + \cdots + c_Nx_N \end{align}

subject to

## Matrix/Vector format¶

The notation can be simplified by adopting a matrix/vector formulation where

\begin{align} \min_{x\geq 0} c^T x \end{align}

subject to

\begin{align} A_{ub} x \leq b_{ub} \\ A_{eq} x = b_{eq} \end{align}

where $c$, $A_{ub}, b_{ub}$, and $A_{eq}, b_{eq}$, are vectors and matrices of coefficients constructed from the linear expressions given above.

## Example 19.3: Refinery Blending Problem¶

The decision variables are

Variable Description Units
$x_1$ crude #1 bbl/day
$x_2$ crude #2 bbl/day
$x_3$ gasoline bbl/day
$x_4$ kerosine bbl/day
$x_5$ fuel oil bbl/day
$x_6$ residual bbl/day

The overall objective is to maximize profit

\begin{align} \mbox{profit} & = \mbox{income} - \mbox{raw_material_cost} - \mbox{processing_cost} \end{align}

where the financial components are given by

\begin{align} \mbox{income} & = 72x_3 + 48x_4 + 42x_5 + 20x_6 \\ \mbox{raw_material_cost} & = 48x_1 + 30x_2 \\ \mbox{processing_cost} & = 1 x_1 + 2x_2 \end{align}

Combining these terms, the objective is to maximize

\begin{align} f & = c^T x = - 49 x_1 - 32 x_2 + 72 x_3 + 48x_4 + 42x_5 + 20x_6 \end{align}

The material balance equations are

\begin{align} \mbox{gasoline: } x_3 & = 0.80 x_1 + 0.44 x_2 \\ \mbox{kerosine: } x_4 & = 0.05 x_1 + 0.10 x_2 \\ \mbox{fuel oil: } x_5 & = 0.10 x_1 + 0.36 x_2 \\ \mbox{residual: } x_6 & = 0.05 x_1 + 0.10 x_2 \end{align}

Production limits

\begin{align} \mbox{gasoline: } x_3 & \leq 24,000 \\ \mbox{kerosine: } x_4 & \leq 2,000 \\ \mbox{fuel oil: } x_5 & \leq 6,000 \end{align}

\begin{align} \underbrace{\left[\begin{array}{cccccc} 0.80 & 0.44 & -1 & 0 & 0 & 0 \\ 0.05 & 0.10 & 0 & -1 & 0 & 0 \\ 0.10 & 0.36 & 0 & 0 & -1 & 0 \\ 0.05 & 0.10 & 0 & 0 & 0 & -1 \end{array}\right]}_{A_{eq}} \left[\begin{array}{c} x_1 \\ x_2 \\ x_3 \\ x_4 \\ x_5 \\ x_6 \end{array}\right] & = \underbrace{\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}\right]}_{b_{eq}} \end{align}

In [10]:
from scipy.optimize import linprog

c = [49, 32, -72, -48, -42, -20]

A_ub = [[0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0]]

b_ub = [24000, 2001, 6000]

A_eq = [[0.80, 0.44, -1,  0,  0,  0],
[0.05, 0.10,  0, -1,  0,  0],
[0.10, 0.36,  0,  0, -1,  0],
[0.05, 0.10,  0,  0,  0, -1]]

b_eq = [0, 0, 0, 0]

results = linprog(c, A_ub, b_ub, A_eq, b_eq)
results
p0 = 573517.24

additional profit 175.035862069

In [11]:
print(results.message)
if results.success:
for k in range(len(results.x)):
print('x[{0:2d}] = {1:7.1f} bbl/day'.format(k+1, results.x[k]))

Optimization terminated successfully.
x[ 1] = 26199.3 bbl/day
x[ 2] =  6910.3 bbl/day
x[ 3] = 24000.0 bbl/day
x[ 4] =  2001.0 bbl/day
x[ 5] =  5107.7 bbl/day
x[ 6] =  2001.0 bbl/day