Le problème du vendeur de journaux (à une période de temps)
\n",
"$\\newcommand\\indi[1]{{\\mathbf 1}_{\\displaystyle #1}}$\n",
"$\\newcommand\\inde[1]{{\\mathbf 1}_{\\displaystyle\\left\\{ #1 \\right\\}}}$\n",
"$\\newcommand{\\ind}{\\inde}$\n",
"$\\newcommand\\E{{\\mathbf E}}$\n",
"$\\newcommand\\Cov{{\\mathrm Cov}}$\n",
"$\\newcommand\\Var{{\\mathrm Var}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chaque matin, le vendeur doit décider d’un nombre de journaux à commander $u \\in \\mathbb{U}= \\{0, 1,\\ldots\\}$ au prix unitaire $c > 0$. La demande du jour est incertaine $w \\in \\mathbb{W} = \\{0, 1,\\ldots\\}$.\n",
"\n",
"Si à la fin de la journée il lui reste des invendus (coût unitaire $c_S \\in \\mathbb{R}$):\n",
"$c_S(u − w )_+ = c_S \\max (u − w,0)$\n",
"\n",
"On suppose que $c > − c_S$.\n",
"\n",
"Si à la fin de la journée il n’a pas pu faire face à la demande on associe un coût unitaire $c_M$. Le coût lié à la non satisfaction de la demande est:\n",
"$c_M (w − u )_+ = c_M \\max (w − u,0)$.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np;\n",
"from scipy.special import comb;\n",
"from scipy.special import gamma;\n",
"import matplotlib.pyplot as plt\n",
"import io;"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {},
"outputs": [],
"source": [
"binomiale=1\n",
"discrete=2\n",
"poisson=3\n",
"\n",
"def choisir_loi(law):\n",
"\n",
" if law == binomiale: ## loi binomiale\n",
" n=100\n",
" p=0.5\n",
"\n",
" buf = io.StringIO()\n",
" buf.write(\"loi binomiale(%d,%5.2f)\" % (n,p))\n",
" title = buf.getvalue()\n",
" \n",
" wi = np.linspace(0,n,num=n+1) ## les valeurs possibles {0,1,...,n}\n",
" ## pdf(\"bin\",x,n,p) = n!/x!(n-x)! p^x (1-p)^n-x \n",
" nn = n*np.ones(n+1)\n",
" pi=comb(nn,wi, exact=False) * pow(p,wi)*pow(1-p,n-wi)\n",
"\n",
" mu=n*p; ## moyenne de la binomiale \n",
" mu1= sum(pi*wi) ; ## vérification \n",
" if abs(mu-mu1) > 1.e-8:\n",
" print(\"something wrong in binomial law expectation\")\n",
" wait = input(\"PRESS ENTER TO CONTINUE.\")\n",
" \n",
" # un echantillong de taille N\n",
" N=1000\n",
" #W=grand(1,N,\"bin\",n,p);\n",
" W = np.random.binomial(n, p, N)\n",
" \n",
" if law == discrete: ## une loi discrète \n",
" n=3\n",
" wi=np.array([30,50,80])\n",
" # A vous\n",
" # pi = choisir des probabilités\n",
" buf = io.StringIO()\n",
" buf.write(\"loi discrète sur %d valeurs\" % wi.size)\n",
" title = buf.getvalue()\n",
" # A vous : mu= ## la moyenne\n",
"\n",
" N=1000\n",
" # un echantillong de taille N\n",
" # A vous : tirer un échantillon de taille N\n",
" \n",
" if law == poisson: ## loi de Poisson de paramètre mu \n",
" n=100;\n",
" p=0.5;\n",
" mu=n*p;\n",
" wi=np.linspace(0,n,num=n+1); ## les valeurs possibles \n",
" pi=( pow(mu,wi) *np.exp(-mu)) / gamma(wi+1);\n",
" buf = io.StringIO();\n",
" buf.write(\"Poisson %f\" % mu);\n",
" title = buf.getvalue();\n",
" # A vous : \n",
" # calculer la moyenne theorique, \n",
" # tirer un echantillon de taille N, \n",
" # comparer avec la moyenne empirique\n",
"\n",
" return pi, wi, W, title\n",
"\n",
"def hist_plot(samples,support=[]):\n",
" if np.size(support)==0:\n",
" support = np.sort(list(dict.fromkeys(samples)))\n",
" histo=np.zeros(support.size);\n",
" for i in range(support.size):\n",
" histo[i]=np.size(np.where(samples==support[i]))/(W.size)\n",
" # On trace cet histogramme\n",
" plt.bar(support, histo,width=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pi, wi, W, title = choisir_loi(poisson) # poisson, binomiale, discrete\n",
"\n",
"fig = plt.gcf()\n",
"\n",
"#plt.subplot(1,3,1);\n",
"plt.bar(wi,pi,width=1)\n",
"plt.title(\"loi\"); \n",
"plt.show()\n",
"\n",
"#plt.subplot(1,3,2);\n",
"plt.step(wi,np.cumsum(pi),where='post',marker='o',markerfacecolor='r');\n",
"plt.title(\"cdf :\" + title)\n",
"plt.show()\n",
"\n",
"#plt.subplot(1,3,3);\n",
"hist_plot(W,wi)\n",
"plt.title(\"loi empirique\"); \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Paragraph 1.2__ On utilise tout d’abord la distribution de Poisson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__1.1 La demande aléatoire__\n",
"\n",
"On veut faire tourner le même code pour plusieurs lois distinctes qui seront des lois discrètes. La valeur de law permet de selctionner une loi au choix: loi _binomiale_, loi _discrete_, loi de _Poisson_.\n"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {},
"outputs": [],
"source": [
"pi, wi, W, title = choisir_loi(poisson)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 2.__ Écrire une fonction Python qui calcule j(u,w) puis une fonction qui calcule J(u). Faire un graphique avec les valeurs proposées des constantes et calculer le nombre de journaux qu’il faut commander "
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cas discrêt: Nombre optimal de journaux a commander *30.000000*, Moyenne de la demande *47.500000*\n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cas binomial: Nombre optimal de journaux a commander *49.000000*, Moyenne de la demande *50.000000*\n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cas Poisson: Nombre optimal de journaux a commander *48.000000*, Moyenne de la demande *50.000000*\n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c=10;cm=20;cs=5;cf=200;\n",
"\n",
"# la fonction coût j(u,w)\n",
"def jj(u,w):\n",
" # A vous : écrire la fonction j (voir slides)\n",
"\n",
"\n",
"def J(u,pi,wi):\n",
" # A vous : écrire la fonction J = la moyenne de j (voir slides)\n",
"\n",
"\n",
"def resultats(pi,wi,dessin='oui'):\n",
"# draw the function and the minimum \n",
"\n",
" # remplir val avec les valeurs de J(u) quand u varie de -10 a 100 avec un pas de 1\n",
" u=np.linspace(-10,100,num=111)\n",
" val=np.zeros(u.size);\n",
" for i in range(0,u.size):\n",
" # A vous : val[i]= ??\n",
" # val[i]= # modifier\n",
" \n",
" # recherche du min \n",
" # A vous : trouver la valeur uopt\n",
" # uopt = ?\n",
" \n",
" if dessin=='oui':\n",
" plt.plot(u,val);\n",
" plt.title(\"fonction coût\");\n",
" plt.plot(uopt,val[imin],marker='o',markerfacecolor='r')\n",
"\n",
" print(\"Nombre optimal de journaux a commander *%f*, \" % uopt,end='');\n",
" print(\"Moyenne de la demande *%f*\\n\" % sum(wi*pi));\n",
"\n",
" return uopt\n",
"\n",
"\n",
"pi, wi, W, title = choisir_loi(discrete) # poisson, binomiale, discrete\n",
"print('Cas discrêt: ',end='')\n",
"resultats(pi,wi)\n",
"plt.show()\n",
" \n",
"pi, wi, W, title = choisir_loi(binomiale) # poisson, binomiale, discrete\n",
"print('Cas binomial: ',end='')\n",
"resultats(pi,wi)\n",
"plt.show()\n",
"\n",
"pi, wi, W, title = choisir_loi(poisson) # poisson, binomiale, discrete\n",
"print('Cas Poisson: ',end='')\n",
"resultats(pi,wi)\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 3.__ Vérifier sur un graphique que le nombre de journaux optimal à commander s’obtient par la formule :\n",
"\n",
"$$\\text{uopt} = \\inf\\left\\{z ∈ ℝ |F (z) ≥ (c_M − c)∕ (c_M + c_S )\\right\\}$$\n"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 239,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pi, wi, W, title = choisir_loi(binomiale) # poisson, binomiale, discrete\n",
"\n",
"# On dessine F \n",
"fstar = (cm-c)/(cm+cs) # valeur limite\n",
"Fv= np.cumsum(pi) # calcul de la fonction de repartition\n",
"plt.clf()\n",
"xx=np.hstack((0,wi)) # on rajoute 0 a wi\n",
"yy=np.hstack((0,Fv)) # on rajoute 0 a Fv\n",
"plt.step(xx,yy,where='post') # tracé de la fonction de répartition\n",
"plt.plot(xx,fstar*np.ones(xx.size)) # droite horizontale de valeur fstar\n"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# chercher l'indice qui donne uopt avec la formule du cours\n",
"kopt=0\n",
"for i in range(0,Fv.size):\n",
" if Fv[i] >= fstar:\n",
" kopt=i\n",
" break;\n",
"\n",
"plt.step(xx,yy,where='post') # marker='o',markerfacecolor='r');\n",
"plt.plot(xx,fstar*np.ones(xx.size))\n",
"plt.plot(wi[kopt],Fv[kopt],marker='o',markeredgewidth=5,markeredgecolor='r',markerfacecolor='r');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Paragraphe 1.3._ On utilise maintenant une loi discrète à 3 valeurs (law=2)\n",
"\n",
"Dans le cas précédent, on trouve que le nombre de journaux optimal à commander est très voisin de la moyenne de la demande. On cherche ici à construire un exemple ou les deux nombres seront franchement différents."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Question 4.__ Reprendre ce qui précède, en vous plaçant maintenant dans le cas 2 et chercher à caler des valeurs des probabilités qui permettent d’obtenir le résultat souhaité."
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre optimal de journaux a commander *10.000000*, Moyenne de la demande *23.750000*\n",
"\n"
]
},
{
"data": {
"text/plain": [
"10.0"
]
},
"execution_count": 241,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"wi=np.array([10,50,80])\n",
"pi=np.array([3/4,1/8,1/8])\n",
"\n",
"resultats(pi,wi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Paragraphe 1.4_ La loi du coût\n",
"\n",
"__Question 5.__ Dans les cas 1 (binomiale) et cas 2 (discrete), faites un graphique de la loi du coût pour diverses valeurs de la commande $u$. On procédera de deux façons différentes\n",
" - En calculant la loi du coût.\n",
" - En approchant la loi du coût au moyen de tirages de la demande (loi empirique des coûts)."
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"52\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 246,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"\n",
"def draw_cost(u,W):\n",
" #count, bins, ignored = plt.hist(j(u,W),density=True)\n",
" samples=jj(u,W)\n",
" max_samples=max(samples)\n",
" hist_plot(samples)\n",
" return max_samples\n",
"\n",
"# poisson , discrete, binomiale\n",
"pi, wi, W, title = choisir_loi(binomiale)\n",
"\n",
"u=49\n",
"# commence par traiter le problème par simulation (avec W)\n",
"max_samples=draw_cost(u,W);\n",
"plt.show()\n",
"\n",
"# calcul de la loi exacte\n",
"valeurs=jj(u,wi) # toutes les valeurs de j(u,wi[i])\n",
"support = np.sort(list(dict.fromkeys(valeurs))) \n",
" # on calcule le support de la loi\n",
" # en enlevant les valeurs en double\n",
"loi=np.zeros(support.size) # on va calculer la loi de j(u,W)\n",
"for i in range(support.size):\n",
" for k in range(wi.size):\n",
" if jj(u,wi[k]) == support[i]:\n",
" loi[i]=loi[i]+pi[k]\n",
"\n",
"# on tronque l'histogramme au delà de la valeur max_samples\n",
"for imax in range(support.size):\n",
" if support[imax] > max_samples:\n",
" print(imax);break\n",
"imax=imax-1\n",
" \n",
"# On trace cet histogramme\n",
"plt.bar(support[0:imax], loi[0:imax],width=5)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Paragraphe 1.6.__ Stratégie $[s,S]$\n",
"\n",
"On regarde maintenant un cas ou le vendeur à déjà des journaux et où il paye un cout fixe s'il commande des journaux. \n",
"On cherche à retrouver ici le fait que la stratégie optimale est de la forme $[s,S]$.\n",
"\n",
"__Question 7.__ On se placera dans le cas de la loi binomiale. Calculer le nombre optimal de journaux à commander \n",
"suivant la valeur du stock initial. \n",
"Vérifier que la stratégie est bien de la forme $[s,S]$: \n",
"on remonte le stock au niveau $S$ si il est inférieur à $s$ et on ne fait rien sinon. \n",
"\n",
"On vérifie que $s$ se calcule aussi par la formule\n",
"$$s := \\sup \\left\\{z ∈ (− ∞, S)|J(z) ≥ cF + J(S )\\right\\} $$"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1) Calcul par une méthode brutale : valeur de s=23.000000 et de S=48.000000\n",
"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2) Formule du cours: valeur de s=23.000000 et de S=48.000000\n",
"\n"
]
}
],
"source": [
"# On regarde maintenant un cas ou le vendeur à déjà des journaux \n",
"# et ou il paye un cout fixe s'il commande des journaux \n",
"# on voit une stratégie [s,S] \n",
"\n",
"def Jtilde(u,x,pi,wi):\n",
" return cf*(u>0) + J(u+x,pi,wi) -c*x\n",
"\n",
"# Le but est de verifier que l'on a une stratégie [s,S]\n",
"\n",
"# Coix du modèle : poisson , discrete, binomiale\n",
"pi, wi, W, title = choisir_loi(poisson)\n",
"\n",
"# Calcul de S\n",
"# S=uopt, où uopt est l'argmin de J(u) (cf Slide 11/20)\n",
"# uopt est caclulé dans la fonction résultat\n",
"uopt=resultats(pi,wi,dessin='non') # on ne veux pas de dessin ici\n",
"S=uopt \n",
"\n",
"# Il nous faut maintenant calculer s\n",
"xv=np.linspace(0,2*max(wi), num=200+1);\n",
"xuopt=xv.copy();\n",
"U=np.linspace(0,2*max(wi), num=200+1); # \n",
"Ju=U.copy();\n",
"\n",
"# A vous : pour chaque valeur du stock xv[i], calculer le cout avec la commande U[j]\n",
"# puis chercher la commande optimale que vous rangez dans xuopt[i]\n",
"# for i in range(0, xv.size):\n",
"# for j in range(0, U.size):\n",
"\n",
"# s est la valeur la plus grande où le contrôle est non nul\n",
"# on cherche donc l'indice iopt de la valeur la plus petite où le \n",
"# contrôle est non nul. xv[iopt-1] donne la valeur de s cherchée.\n",
"# A vous : \n",
"#iopt=-1;\n",
"#for i in range(0, xuopt.size):\n",
"# (iopt-1) est le dernier indice ou la controle est non nul\n",
"# et s est la valeur de x correspondante xv[iopt-1]\n",
"s = xv[iopt-1]\n",
"print(\"1) Calcul par une méthode brutale : valeur de s=%f et de S=%f\\n\" % (s, S))\n",
"\n",
"plt.clf()\n",
"\n",
"# on vérifie que pour x en dessous de s, x+uopt=cte=S\n",
"plt.plot(xv[:int(s)],xv[:int(s)]+xuopt[:int(s)])\n",
"\n",
"# au dela xuopt=0 (on ne commande rien), donc on est sur la droite y = x!\n",
"plt.plot(xv[int(s)+1:150],xv[int(s)+1:150]+xuopt[int(s)+1:150])\n",
"\n",
"plt.show()\n",
"\n",
"# On vérifie le formule de s donnée dans le cours (p.11 cours-4) fonctionne\n",
"xv= np.linspace(0,2*max(wi), num=200+1);\n",
"Jv=np.zeros(xv.size);\n",
"for i in range(0,xv.size):\n",
" Jv[i]=J(xv[i],pi,wi)\n",
"JS=J(S,pi,wi) # J(S)\n",
"costs = Jv - (cf + JS);\n",
"# calcul du plus grand z où J(z) >= c_F + J(S)\n",
"iopt=-1\n",
"for i in range(0,xv.size):\n",
" if costs[i] <=0:\n",
" iopt=i;\n",
" break;\n",
"if iopt < 0: \n",
" s=0 \n",
"else:\n",
" s=xv[iopt-1]\n",
" \n",
"print(\"2) Formule du cours: valeur de s=%f et de S=%f\\n\" % (s, S))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Question 6 En utilisant la présentation faite en cours, construire un problème linéaire dont la solution \n",
" permet de calculer le nombre de journaux optimal à commander "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}