![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\" "\\"Open"){kind=icon}
![\"Download\"](\"https://img.shields.io/badge/Github-Download-blue.svg\" "\\"Download"){kind=icon}
"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 1,
"link": "[6.2 Linear Production Model](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2-Linear-Production-Model)",
"section": "6.2 Linear Production Model"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 6.2 Linear Production Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 1,
"link": "[6.2 Linear Production Model](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2-Linear-Production-Model)",
"section": "6.2 Linear Production Model"
}
},
"source": [
"This notebook demonstrates the use of linear programming to maximize profit for a simple model of a multiproduct production facility. The notebook uses [Pyomo](http://www.pyomo.org/) to represent the model with the [glpk](https://en.wikibooks.org/wiki/GLPK) solver to calculate solutions."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.1 Example: Production Plan for a Single Product Plant](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1-Example:-Production-Plan-for-a-Single-Product-Plant)",
"section": "6.2.1 Example: Production Plan for a Single Product Plant"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 6.2.1 Example: Production Plan for a Single Product Plant"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.1 Example: Production Plan for a Single Product Plant](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1-Example:-Production-Plan-for-a-Single-Product-Plant)",
"section": "6.2.1 Example: Production Plan for a Single Product Plant"
}
},
"source": [
"Suppose you are thinking about starting up a business to produce Product X. You have determined there is a market for X of up to 40 units per week at a price of \\$270 each. The production of each unit requires \\$100 of raw materials, 1 hour of type A labor, and 2 hours of type B labor. You have an unlimited amount of raw material available to you, but only 80 hours per week of labor A at a cost of \\$50/hour, and 100 hours per week of labor B at a cost of \\$40 per hour. Ignoring all other expenses, what is the maximum weekly profit?\n",
"\n",
"To get started on this problem, we sketch a flow diagram illustrating the flow of raw materials and labor through the production plant."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.1 Example: Production Plan for a Single Product Plant](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1-Example:-Production-Plan-for-a-Single-Product-Plant)",
"section": "6.2.1 Example: Production Plan for a Single Product Plant"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.1 Example: Production Plan for a Single Product Plant](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1-Example:-Production-Plan-for-a-Single-Product-Plant)",
"section": "6.2.1 Example: Production Plan for a Single Product Plant"
}
},
"source": [
"The essential decision we need to make is how many units or Product X to produce each week. That's our *decision variable* which we denote as $x$. The weekly revenues are then\n",
"\n",
"$$ \\mbox{Revenue} = \\$270 x $$\n",
"\n",
"The costs include the value of the raw materials and each form of labor. If we produce x units a week, then the total cost is\n",
"\n",
"$$ \\mbox{Cost} = \\underbrace{\\$100 x}_{\\mbox{Raw Material}} \n",
" + \\underbrace{\\$50 x}_{\\mbox{Labor A}} + \\underbrace{2\\times\\$40 x}_{\\mbox{Labor B}} = \\$230 x$$\n",
" \n",
"We see immediately that the gross profit is just\n",
"\n",
"$$\\begin{eqnarray*}\\mbox{Profit} & = & \\mbox{Revenue} - \\mbox{Cost} \\\\\n",
"& = & \\$270x - \\$230x \\\\\n",
"& = & \\$40 x\n",
"\\end{eqnarray*}$$\n",
"\n",
"which means there is a profit earned on each unit of X produced, so let's produce as many as possible. \n",
"\n",
"There are three constraints that limit how many units can be produced. There is market demand for no more than 40 units per week. Producing $x = 40$ units per week will require 40 hours per week of Labor A, and 80 hours per week of Labor B. Checking those constraints we see that we have enough labor of each type, so the maximum profit will be\n",
"\n",
"$$\\max \\mbox{Profit} = $40 \\mbox{ per unit} \\times 40 \\mbox{ units per week} = \\$1600 \\mbox{ per week}$$\n",
"\n",
"What we conclude is that market demand is the 'most constraining constraint.' Once we've made that deduction, the rest is a straightforward problem that can be solved by inspection. "
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[6.2.1.1 Mathematical Model](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1.1-Mathematical-Model)",
"section": "6.2.1.1 Mathematical Model"
}
},
"source": [
"### 6.2.1.1 Mathematical Model\n",
"\n",
"Even though this example has a straightforward solution, it is useful to consider how it can be represented mathematically, and solved using typical tools for linear programming.\n",
"\n",
"The mathematical representation consists of a single non-negative decision variable, $x$, an objective function, and a set of linear constraints. Here we include all constraints even though we know only one of them -- the most 'constraining constraint' -- will be active.\n",
"\n",
"\\begin{align}\n",
"\\max_{x \\geq 0} &\\ 40\\ x & \\mbox{objective}\\\\\n",
"\\mbox{subject to:}\\qquad \\\\\n",
"x & \\leq 40 & \\mbox{demand constraint} \\\\\n",
"x & \\leq 80 & \\mbox{labor A constraint} \\\\\n",
"2\\ x & \\leq 100 & \\mbox{labor B constraint}\n",
"\\end{align}\n",
"\n",
"All of these constraints must be satisfied, therefore the demand constraint is the 'most constrainting'. Again, the maximum value of $x$ is 40, so the maximum profit is $\\$ 40 \\times 40 = \\$1,600$."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[6.2.1.2 Exercises](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1.2-Exercises)",
"section": "6.2.1.2 Exercises"
}
},
"source": [
"### 6.2.1.2 Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[6.2.1.2 Exercises](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.1.2-Exercises)",
"section": "6.2.1.2 Exercises"
}
},
"source": [
"1. Suppose the demand could be increased to 50 units per month. What would be the increased profits? What if the demand increased to 60 units per month? How much would you be willing to pay for your marketing department for the increased demand?\n",
"\n",
"2. Increase the cost of LaborB. At what point is it no longer financially viable to run the plant?"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.2 Production Plan: Product Y](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2-Production-Plan:-Product-Y)",
"section": "6.2.2 Production Plan: Product Y"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 6.2.2 Production Plan: Product Y"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.2 Production Plan: Product Y](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2-Production-Plan:-Product-Y)",
"section": "6.2.2 Production Plan: Product Y"
}
},
"source": [
"Your marketing department has developed plans for a new product called Y. The product sells at a price of \\\\$210 each, and they expect that you can sell all that you can make. It's also cheaper to make, requiring only \\\\$90 in raw materials, 1 hour of Labor type A at \\\\$50 per hour, and 1 hour of Labor B at \\\\$40 per hour. What is the potential weekly profit?"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.2 Production Plan: Product Y](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2-Production-Plan:-Product-Y)",
"section": "6.2.2 Production Plan: Product Y"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.2 Production Plan: Product Y](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2-Production-Plan:-Product-Y)",
"section": "6.2.2 Production Plan: Product Y"
}
},
"source": [
"The analysis proceeds in the same form as Product X. In this case the revenue is given by\n",
"\n",
"$$ \\mbox{Revenue} = \\$210 y $$\n",
"\n",
"The total cost is\n",
"\n",
"$$ \\mbox{Cost} = \\underbrace{\\$90 x}_{\\mbox{Raw Material}} \n",
" + \\underbrace{\\$50 x}_{\\mbox{Labor A}} + \\underbrace{\\$40 x}_{\\mbox{Labor B}} = \\$180 x$$\n",
" \n",
"The gross profit is thn\n",
"\n",
"\\begin{eqnarray}\\mbox{Profit} & = & \\mbox{Revenue} - \\mbox{Cost} \\\\\n",
"& = & \\$210x - \\$180x \\\\\n",
"& = & \\$30 x\n",
"\\end{eqnarray}\n",
"\n",
"We see the profit per unit of Y is smaller. So a decision to produce Y instead of X must be based on the ability to make Y in larger quantities.\n",
"\n",
"The mathematical formulation of this problem becomes\n",
"\n",
"\\begin{align}\n",
"\\max_{y \\geq 0} &\\ 30\\ y & \\mbox{objective}\\\\\n",
"\\mbox{subject to:}\\qquad \\\\\n",
"y & \\leq 80 & \\mbox{labor A constraint} \\\\\n",
"y & \\leq 100 & \\mbox{labor B constraint}\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.2 Production Plan: Product Y](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2-Production-Plan:-Product-Y)",
"section": "6.2.2 Production Plan: Product Y"
}
},
"source": [
"Compared to product X, we can manufacture and sell up 80 units per week for a total profit of \\$2,400. This is very welcome news. "
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 3,
"link": "[6.2.2.1 Exercises](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.2.1-Exercises)",
"section": "6.2.2.1 Exercises"
}
},
"source": [
"### 6.2.2.1 Exercises\n",
"\n",
"1. What is the limiting resource? That is, which of the two types of labor limits the capacity of your plant to produce more units of Y?\n",
"\n",
"2. What rate would you be willing to pay for the additional labor necessary to increase the production of Y?"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.3 Production Plan: Mixed Product Strategy](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.3-Production-Plan:-Mixed-Product-Strategy)",
"section": "6.2.3 Production Plan: Mixed Product Strategy"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 6.2.3 Production Plan: Mixed Product Strategy"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.3 Production Plan: Mixed Product Strategy](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.3-Production-Plan:-Mixed-Product-Strategy)",
"section": "6.2.3 Production Plan: Mixed Product Strategy"
}
},
"source": [
"So far we have learned that we can make \\\\$1,600 per week by manufacturing product X, and \\\\$2,400 per week manufacturing product Y. Is it possible to do even better?\n",
"\n",
"To answer this question, we consider the possibilty of manufacturing both products in the same plant. The marketing department assures us that product Y will not affect the sales of product X. So the same constraints hold as before, but now we have two decision variables, $x$ and $y$."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.3 Production Plan: Mixed Product Strategy](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.3-Production-Plan:-Mixed-Product-Strategy)",
"section": "6.2.3 Production Plan: Mixed Product Strategy"
}
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.3 Production Plan: Mixed Product Strategy](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.3-Production-Plan:-Mixed-Product-Strategy)",
"section": "6.2.3 Production Plan: Mixed Product Strategy"
}
},
"source": [
"Mathematical formulation\n",
"\n",
"\\begin{align}\n",
"\\max_{x,y \\geq 0} &\\ 40\\ x + 30\\ y & \\mbox{objective}\\\\\n",
"\\mbox{subject to:}\\qquad \\\\\n",
"x & \\leq 40 & \\mbox{demand constraint} \\\\\n",
"x + y & \\leq 80 & \\mbox{labor A constraint} \\\\\n",
"2x + y & \\leq 100 & \\mbox{labor B constraint}\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.4 Solution using scipy.optimize.linprog](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.4-Solution-using-scipy.optimize.linprog)",
"section": "6.2.4 Solution using scipy.optimize.linprog"
}
},
"source": [
"## 6.2.4 Solution using scipy.optimize.linprog\n",
"\n",
"The [scipy.optimize library](https://docs.scipy.org/doc/scipy/reference/optimize.html) includes a comprehensive collection of functions for finding roots and minimizing systems of one or more equations. Given a new problem, this library is often a good place to look for tools needed to solve a system of equations, or to minimize a particular objective.\n",
"\n",
"In particular, the function `scipy.optimize.linprog` solves linear programming problems represented in the form\n",
"\n",
"\\begin{align}\n",
"\\min_{x \\geq 0} &\\ c^T x \\\\\n",
"\\mbox{subject to:}\\qquad \\\\\n",
"A_{ub} & \\leq b_{ub} \\\\\n",
"A_{eq} & = b_{eq} \\\\\n",
"\\end{align}\n",
"\n",
"where $c$ is a vector of coefficients in the objective function to be minimized, $A_{ub}$ and $b_{ub}$ are the coefficients and right-hand-sides of problem constraints written as upper bounds, and $A_{eq}$ and $b_{eq}$ are coefficients and right-hand-sides for equality constraints."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.4 Solution using scipy.optimize.linprog](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.4-Solution-using-scipy.optimize.linprog)",
"section": "6.2.4 Solution using scipy.optimize.linprog"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
"x = [ 20. 60.]\n",
"objective = -2600.0\n"
]
}
],
"source": [
"from scipy.optimize import linprog\n",
"\n",
"c = [-40, -30]\n",
"\n",
"A_ub = [[1, 0], \n",
" [1, 1], \n",
" [2, 1]]\n",
"\n",
"b_ub = [40, \n",
" 80, \n",
" 100]\n",
"\n",
"results = linprog(c, A_ub, b_ub)\n",
"\n",
"print(results.message)\n",
"if results.success:\n",
" print('x =', results.x)\n",
" print('objective = ', results.fun)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.4 Solution using scipy.optimize.linprog](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.4-Solution-using-scipy.optimize.linprog)",
"section": "6.2.4 Solution using scipy.optimize.linprog"
}
},
"source": [
"The mixed product strategy earns more profit than either of the single product srategies. Does this surprise you? Before going further, try to explain why it is possible for a mixed product strategy to earn more profit than either of the possible single product strategies."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.5 What are the active constraints?](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.5-What-are-the-active-constraints?)",
"section": "6.2.5 What are the active constraints?"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 6.2.5 What are the active constraints?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"nbpages": {
"level": 2,
"link": "[6.2.5 What are the active constraints?](https://jckantor.github.io/CBE30338/06.02-Linear-Production-Model.html#6.2.5-What-are-the-active-constraints?)",
"section": "6.2.5 What are the active constraints?"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}