{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Asset replacement model with maintenance\n", "\n", "**Randall Romero Aguilar, PhD**\n", "\n", "This demo is based on the original Matlab demo accompanying the Computational Economics and Finance 2001 textbook by Mario Miranda and Paul Fackler.\n", "\n", "Original (Matlab) CompEcon file: **demddp03.m**\n", "\n", "Running this file requires the Python version of CompEcon. This can be installed with pip by running\n", "\n", " !pip install compecon --upgrade\n", "\n", "Last updated: 2021-Oct-01\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About\n", "\n", "Consider the preceding example, but suppose that the productivity of the asset may be\n", "enhanced by performing annual service maintenance. Specifically, at the beginning of each\n", "year, a manufacturer must decide whether to replace the asset with a new one or, if he elects\n", "to keep the asset, whether to service it. An asset that is $a$ years old and has been serviced $s$ times yields a profit contribution $p(a, s)$ up to an age of $n$ years, at which point the asset becomes unsafe and must be replaced by law. The cost of a new asset is $c$, and the cost of servicing an asset is $k$. What replacement-maintenance policy maximizes profits?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial tasks" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from compecon import DDPmodel, gridmake, getindex" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Parameters\n", "Assume a maximum asset age of 5 years, asset replacement cost $c = 75$, cost of servicing $k = 10$, and annual discount factor $\\delta = 0.9$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "maxage = 5\n", "repcost = 75\n", "mancost = 10\n", "delta = 0.9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### State Space\n", "\n", "This is an infinite horizon, deterministic model with time $t$ measured in years. The state\n", "variables\n", "\n", "$a \\in \\{1, 2, 3, \\dots, n\\}$\n", "\n", "$s \\in \\{0, 1, 2, \\dots, n − 1\\}$\n", "\n", "are the age of the asset in years and the number of servicings it has undergone, respectively." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "s1 = np.arange(1, 1 + maxage) # asset age\n", "s2 = s1 - 1 # servicings\n", "S = gridmake(s1,s2) # combined state grid\n", "S1, S2 = S\n", "n = S1.size # total number of states" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the set of possible asset ages and servicings are generated individually, and then a two-dimensional state grid is constructed by forming their Cartesian product using the CompEcon routine gridmake." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Action Space\n", "The action variable\n", "\n", "$x \\in \\{\\text{no action}, \\text{service}, \\text{replace}\\}$\n", "\n", "is the hold-replacement-maintenance decision." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = np.array(['no action', 'service', 'replace']) # vector of actions\n", "m = len(X) # number of actions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reward Function\n", "The reward function is\n", "\\begin{equation}\n", "f (a, s, x) =\n", "\\begin{cases}\n", "p(a, s), &x = \\text{no action}\\\\\n", "p(0, 0) − c, &x = \\text{service}\\\\\n", "p(a, s + 1) − k, &x = \\text{replace}\n", "\\end{cases}\n", "\\end{equation}\n", "\n", "Assuming a profit contribution $p(a) =\n", "50 − 2.5a −2.5a^2$ that is a function of the asset age $a$ in years.\n", "\n", "Here, the rows of the reward matrix, which\n", "correspond to the three admissible decisions (no action, service, replace), are computed\n", "individually." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "f = np.zeros((m, n))\n", "q = 50 - 2.5 * S1 - 2.5 * S1 ** 2\n", "f[0] = q * np.minimum(1, 1 - (S1 - S2) / maxage)\n", "f[1] = q * np.minimum(1, 1 - (S1 - S2 - 1) / maxage) - mancost\n", "f[2] = 50 - repcost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### State Transition Function\n", "The state transition function is\n", "\\begin{equation}\n", "g(a, s, x) =\n", "\\begin{cases}\n", "(a + 1, s), &x = \\text{no action}\\\\\n", " (1, 0), &x = \\text{service}\\\\\n", "(a + 1, s + 1), &x = \\text{replace}\n", "\\end{cases}\n", "\\end{equation}\n", "\n", "Here, the routine ```getindex``` is used to find the index of the following period’s state." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "g = np.empty_like(f)\n", "g[0] = getindex(np.c_[S1 + 1, S2], S)\n", "g[1] = getindex(np.c_[S1 + 1, S2 + 1], S)\n", "g[2] = getindex(np.c_[1, 0], S)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value of asset of age $a$ that has undergone $s$ servicings satisfies the Bellman equation\n", "\\begin{equation}\n", "V(a,s) = \\max\\{p(a,s) + \\delta V(a+1,s),\\quad p(a,s+1)−k + \\delta V(a+1,s+1),\n", "\\quad p(0,0) − c + \\delta V(1,0)\\}\n", "\\end{equation}\n", "\n", "where we set $p(n, s) = −\\infty$ for all $s$ to enforce replacement of an asset of age $n$. The\n", "Bellman equation asserts that if the manufacturer replaces an asset of age $a$ with servicings\n", "$s$, he earns $p(0,0) − c$ over the coming year and begins the subsequent year with an asset\n", "worth $V(1,0)$; if he services the asset, he earns $p(a, s + 1) − k$ over the coming year\n", "and begins the subsequent year with an asset worth $V(a + 1, s + 1)$. As with the previous\n", "example, the value $V(a, s)$ measures not only the current and future net earnings of the\n", "asset, but also the net earnings of all future assets that replace it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To solve and simulate this model, use the CompEcon class ```DDPmodel```. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": "A deterministic discrete state, discrete action, dynamic model.\nThere are 3 possible actions over 25 possible states" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = DDPmodel(f, g, delta)\n", "model.solve()\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate Model\n", "The paths are computed by performing q deterministic simulation of 12 years in duration using the ```simulate()``` method." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sinit = 0\n", "nyrs = 12\n", "t = np.arange(nyrs + 1)\n", "spath, xpath = model.simulate(sinit, nyrs)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": "['service', 'service', 'no action', 'replace', 'service', ..., 'service', 'service', 'no action', 'replace', 'service']\nLength: 13\nCategories (3, object): ['no action', 'service', 'replace']" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Categorical(X[xpath], categories=X)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": " Age of Asset Number of Servicings Action\nYear \n0 1 0 service\n1 2 1 service\n2 3 2 no action\n3 4 2 replace\n4 1 0 service\n5 2 1 service\n6 3 2 no action\n7 4 2 replace\n8 1 0 service\n9 2 1 service\n10 3 2 no action\n11 4 2 replace\n12 1 0 service", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Age of AssetNumber of ServicingsAction
Year
010service
121service
232no action
342replace
410service
521service
632no action
742replace
810service
921service
1032no action
1142replace
1210service
\n
" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simul = pd.DataFrame({\n", " 'Year': t,\n", " 'Age of Asset': S1[spath],\n", " 'Number of Servicings': S2[spath]}).set_index('Year')\n", "\n", "simul['Action'] = pd.Categorical(X[xpath], categories=X)\n", "simul" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot State Paths (Age and Servicings) and Action Path\n", "The asset is replaced every four years, and is serviced twice, at the beginning of the second and third years of operation." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": "
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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(3, 1, sharex=True, figsize=[9,9])\n", "simul['Age of Asset'].plot(marker='o', ax=axs[0])\n", "axs[0].set(title='Age of Asset')\n", "\n", "simul['Number of Servicings'].plot(marker='o', ax=axs[1])\n", "axs[1].set(title='Number of Servicings')\n", "\n", "simul['Action'].cat.codes.plot(marker='o', ax=axs[2])\n", "axs[2].set(title='Action Path', yticks=range(3), yticklabels=X);" ] } ], "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }