Revenue Management¶
Principles learned¶
Add constraints to a model with surrogate modeling
Call an expensive external function
Set bounds for an external function
Add evaluation points to a function
Problem¶
A businessman wants to maximize the income from the sale of a product over a certain time horizon, split into several periods. At the beginning of the time horizon, he has to decide the total amount of product to buy. Then in each period he has to choose the number of units to sell, provided that the total number of units sold during the time horizon does not exceed the initial amount purchased. The price of the product increases over the periods, this means the businessman has to determine the number of units to reserve for customers who arrive later, because they will pay more. The later demand must therefore be considered to make a wise decision at each period. Since the demand for the product is stochastic, the businessman simulates each repartition of units a large number of times to get a robust estimate of his income.
For more details, see: revenue_management.html
Download the exampleProgram¶
Parameters are fixed to the recommended values: 3 periods are used and the initial cost of the product is set to $80.
The demand at each period t
is defined by: Dₜ=μₜXYₜ
, where:
Yₜ
has an exponential distribution with a rate parameterλ=1
.
X
has a gamma distribution with a shape parameterk=1
and a scale parameterθ=1
, which is equivalent to the standard exponential distribution.
μₜ
is the mean demand for this period.
The distributions are implemented in each language. The prices and mean demands at each period are in the table below:
Period |
1 |
2 |
3 |
---|---|---|---|
Price |
100 |
300 |
400 |
Mean demand |
50 |
20 |
30 |
To have a robust estimate of the income, the simulation is realized a large
number of times (1.000.000) using a Monte Carlo method. Each simulation takes
several seconds to run, that’s why the next point to be evaluated has to be
wisely chosen. Thus, an external function is used to compute the average income
from the simulations. To use the surrogate modeling feature, the method enableSurrogateModeling
available on the LSExternalContext
of the function is called.
Three integer decision variables are declared. The first one corresponds to the
initial quantity purchased at the beginning of the time horizon. The second
determines the amount of product that has to be reserved for periods 2 and 3,
and the third the amount of product available for period 3. The domains of these
variables are all [0, 100]
. To ensure the feasibility, each variable is
constrained to be lesser or equal than the previous one.
As the external function is provided by the user, LocalSolver cannot compute
anything about it. It is then useful to parametrize it via the
LSExternalContext
. In this example, the simulation will never return a
negative value, because the prices at any periods are above the initial cost,
and all decision variables are positive. The lower bound of the function is thus
set to 0. The maximum number of evaluations is set to 30 thanks to the
LSSurrogateParameters
.
For this simulation, an evaluation point was previously computed and its value
was saved: the variables [100, 50, 30]
generates a mean revenue of
4740.99
. This point is added with a LSEvaluationPoint
to warm
start the solver.
- Execution:
- localsolver revenue_management.lsp [evaluationLimit=] [solFileName=]
/********** revenue_management.py **********/
use io;
use random;
// Defines input data
function input() {
nbPeriods = 3;
prices = { 100, 300, 400 };
meanDemands = { 50, 20, 30 };
purchasePrice = 80;
evaluatedPoints = {{
"point": { 100, 50, 30 },
"value": 4740.99
}};
nbSimulations = round(1e6);
seed = 1;
// Creates random module
rdm = random.create();
}
// External function
function revenueManagement(v1, v2, v3) {
// Initial quantity purchased
nbUnitsPurchased = v1;
// Number of units that should be left for future periods
nbUnitsReserved = { v2, v3, 0 };
// Sets seed for reproducibility
rdm.init(seed);
// Creates distribution
rateParam = 1.0;
scaleParam = 1.0;
X[0..nbSimulations-1] = gammaSample(scaleParam);
Y[0..nbSimulations-1][0..nbPeriods-1] = exponentialSample(rateParam);
// Runs simulations
sumProfit = 0.0;
for [i in 0..nbSimulations-1] {
remainingCapacity = nbUnitsPurchased;
for [j in 0..nbPeriods-1] {
// Generates demand for period j
demandJ = round(meanDemands[j] * X[i] * Y[i][j]);
nbUnitsSold = min(max(remainingCapacity - nbUnitsReserved[j], 0),
demandJ);
remainingCapacity -= nbUnitsSold;
sumProfit += prices[j] * nbUnitsSold;
}
}
// Calculates mean revenue
meanProfit = sumProfit / nbSimulations;
meanRevenue = meanProfit - purchasePrice * nbUnitsPurchased;
return meanRevenue;
}
function exponentialSample(rateParam) {
u = rdm.nextUniform(0, 1);
return log(1 - u) / (-rateParam);
}
function gammaSample(scaleParam) {
return exponentialSample(scaleParam);
}
function model() {
// Declares decision variables
variables[0..nbPeriods-1] <- int(0, 100);
// Creates the function
funcExpr <- doubleExternalFunction(revenueManagement);
// Calls function
funcCall <- call(funcExpr);
for [i in 0..nbPeriods-1]
funcCall.addOperand(variables[i]);
// Declares constraints
for [i in 1..nbPeriods-1]
constraint variables[i] <= variables[i-1];
// Maximizes function call
maximize funcCall;
// Enables surrogate modeling
context = funcExpr.context;
surrogateParams = context.enableSurrogateModeling();
// Sets lower bound
context.lowerBound = 0.0;
}
function param() {
// Sets the maximum number of evaluations
if (evaluationLimit == nil) surrogateParams.evaluationLimit = 30;
else surrogateParams.evaluationLimit = evaluationLimit;
// Adds evaluation points
for [k in 0..count(evaluatedPoints)-1] {
evaluationPoint = surrogateParams.createEvaluationPoint();
for [i in 0..nbPeriods-1]
evaluationPoint.addArgument(evaluatedPoints[k]["point"][i]);
evaluationPoint.returnValue = evaluatedPoints[k]["value"];
}
}
function output() {
// Writes the solution in a file
if (solFileName != nil) {
local solFile = io.openWrite(solFileName);
solFile.println("obj=", funcCall.value);
solFile.println("b=", variables[0].value);
for [i in 1..nbPeriods-1]
solFile.println("r", i+1, "=", variables[i].value);
}
}
- Execution (Windows)
- set PYTHONPATH=%LS_HOME%\bin\pythonpython revenue_management.py
- Execution (Linux)
- export PYTHONPATH=/opt/localsolver_11_0/bin/pythonpython revenue_management.py
########## revenue_management.py ##########
import localsolver
import sys
import math
import random
class RevenueManagementFunction:
def __init__(self, seed):
self.nb_periods = 3
self.prices = [100, 300, 400]
self.mean_demands = [50, 20, 30]
self.purchase_price = 80
self.evaluated_points = [{
"point": [100, 50, 30],
"value": 4740.99
}]
self.nb_simulations = int(1e6)
self.seed = seed
# External function
def evaluate(self, argument_values):
variables = [argument_values.get(i) for i in range(argument_values.count())]
# Initial quantity purchased
nb_units_purchased = variables[0]
# Number of units that should be left for future periods
nb_units_reserved = variables[1:] + [0]
# Sets seed for reproducibility
random.seed(self.seed)
# Creates distribution
X = [gamma_sample() for i in range(self.nb_simulations)]
Y = [[exponential_sample() for i in range(self.nb_periods)]
for j in range(self.nb_simulations)]
# Runs simulations
sum_profit = 0.0
for i in range(self.nb_simulations):
remaining_capacity = nb_units_purchased
for j in range(self.nb_periods):
# Generates demand for period j
demand_j = int(self.mean_demands[j] * X[i] * Y[i][j])
nb_units_sold = min(max(remaining_capacity - nb_units_reserved[j], 0),
demand_j)
remaining_capacity = remaining_capacity - nb_units_sold
sum_profit += self.prices[j] * nb_units_sold
# Calculates mean revenue
mean_profit = sum_profit / self.nb_simulations
mean_revenue = mean_profit - self.purchase_price * nb_units_purchased
return mean_revenue
def exponential_sample(rate_param=1.0):
u = random.random()
return math.log(1 - u) / (-rate_param)
def gamma_sample(scale_param=1.0):
return exponential_sample(scale_param)
def solve(evaluation_limit, time_limit, output_file):
with localsolver.LocalSolver() as ls:
model = ls.model
# Generates data
revenue_management = RevenueManagementFunction(1)
nb_periods = revenue_management.nb_periods
# Declares decision variables
variables = [model.int(0, 100) for i in range(nb_periods)]
# Creates the function
func_expr = model.create_double_external_function(revenue_management.evaluate)
# Calls function
func_call = model.call(func_expr)
func_call.add_operands(variables)
# Declares constraints
for i in range(1, nb_periods):
model.constraint(variables[i] <= variables[i - 1])
# Maximizes function call
model.maximize(func_call)
# Enables surrogate modeling
context = func_expr.external_context
surrogate_params = context.enable_surrogate_modeling()
# Sets lower bound
context.lower_bound = 0.0
model.close()
# Parametrizes the solver
if time_limit is not None:
ls.param.time_limit = time_limit
# Sets the maximum number of evaluations
surrogate_params.evaluation_limit = evaluation_limit
# Adds evaluation points
for evaluated_point in revenue_management.evaluated_points:
evaluation_point = surrogate_params.create_evaluation_point()
for i in range(nb_periods):
evaluation_point.add_argument(evaluated_point["point"][i])
evaluation_point.set_return_value(evaluated_point["value"])
ls.solve()
# Writes the solution in a file
if output_file is not None:
with open(output_file, 'w') as f:
f.write("obj=%f\n" % func_call.value)
f.write("b=%f\n" % variables[0].value)
for i in range(1, nb_periods):
f.write("r%f=%f\n" % (i + 1, variables[i].value))
if __name__ == '__main__':
output_file = sys.argv[1] if len(sys.argv) > 1 else None
time_limit = int(sys.argv[2]) if len(sys.argv) > 2 else None
evaluation_limit = int(sys.argv[3]) if len(sys.argv) > 3 else 30
solve(evaluation_limit, time_limit, output_file)
- Compilation / Execution (Windows)
- cl /EHsc revenue_management.cpp -I%LS_HOME%\include /link %LS_HOME%\bin\localsolver110.librevenue_management
- Compilation / Execution (Linux)
- g++ revenue_management.cpp -I/opt/localsolver_11_0/include -llocalsolver110 -lpthread -o revenue_managementrevenue_management
/********* revenue_management.cpp *********/
#include <iostream>
#include <fstream>
#include <stdlib.h>
#include <cmath>
#include <vector>
#include "localsolver.h"
using namespace localsolver;
using namespace std;
struct EvaluatedPoint {
public:
EvaluatedPoint(vector<int> point, double value) : point(point), value(value) {}
const int getPoint(int index) { return point[index]; }
const double getValue() { return value; }
private:
vector<int> point;
double value;
};
/* External function */
class RevenueManagementFunction : public LSExternalFunction<lsdouble> {
private:
int seed;
const int nbPeriods = 3;
const int purchasePrice = 80;
const int nbSimulations = (int) 1e6;
vector<EvaluatedPoint> evaluatedPoints;
const int prices(int index) {
const int p[] = {100, 300, 400};
return p[index];
}
const int meanDemands(int index) {
const int d[] = {50, 20, 30};
return d[index];
}
double exponentialSample(double rateParam = 1.0) {
double u = (double) rand() / RAND_MAX;
return log(1 - u) / (-rateParam);
}
double gammaSample(double scaleParam = 1.0) {
return exponentialSample(scaleParam);
}
public:
// Constructor
RevenueManagementFunction(int seed) : seed(seed) {
evaluatedPoints.push_back(EvaluatedPoint({100, 50, 30}, 4740.99));
}
const unsigned int getNbPeriods() { return nbPeriods; }
const vector<EvaluatedPoint> getEvaluatedPoints() { return evaluatedPoints; }
lsdouble call(const LSExternalArgumentValues& argumentValues) override {
// Initial quantity purchased
int nbUnitsPurchased = argumentValues.getIntValue(0);
// Number of units that should be left for future periods
vector<int> nbUnitsReserved(nbPeriods, 0);
for (unsigned int j = 0; j < nbPeriods - 1; j++) {
nbUnitsReserved[j] = argumentValues.getIntValue(j+1);
}
// Sets seed for reproducibility
srand(seed);
// Creates distribution
vector<double> X;
for (unsigned int i = 0; i < nbSimulations; i++) {
X.push_back(gammaSample());
}
vector<vector<double>> Y;
for (unsigned int i = 0; i < nbSimulations; i++) {
vector<double> yt;
for (unsigned int j = 0; j < nbPeriods; j++) {
yt.push_back(exponentialSample());
}
Y.push_back(yt);
}
// Runs simulations
double sumProfit = 0;
for (unsigned int i = 0; i < nbSimulations; i++) {
int remainingCapacity = nbUnitsPurchased;
for (unsigned int j = 0; j < nbPeriods; j++) {
// Generates demand for period j
int demand = (int) (meanDemands(j) * X[i] * Y[i][j]);
int nbUnitsSold = min(max(remainingCapacity - nbUnitsReserved[j], 0),
demand);
remainingCapacity = remainingCapacity - nbUnitsSold;
sumProfit += prices(j) * nbUnitsSold;
}
}
// Calculates mean revenue
double meanProfit = sumProfit / nbSimulations;
double meanRevenue = meanProfit - purchasePrice * nbUnitsPurchased;
return meanRevenue;
}
};
class RevenueManagement {
public:
// Solver
LocalSolver localsolver;
// LS Program variables
vector<LSExpression> variables;
LSExpression funcCall;
void solve(int timeLimit, int evaluationLimit) {
// Declares the optimization model
LSModel model = localsolver.getModel();
// Generates data
RevenueManagementFunction revenueManagement(1);
unsigned int nbPeriods = revenueManagement.getNbPeriods();
// Declares decision variables
for (unsigned int i = 0; i < nbPeriods; i++) {
variables.push_back(model.intVar(0, 100));
}
// Creates the function
LSExpression func = model.createExternalFunction(&revenueManagement);
// Calls function
funcCall = model.call(func);
for (unsigned int i = 0; i < nbPeriods; i++) {
funcCall.addOperand(variables[i]);
}
// Declares constraints
for (unsigned int i = 1; i < nbPeriods; i++) {
model.constraint(variables[i] <= variables[i-1]);
}
// Maximizes function call
model.maximize(funcCall);
// Enables surrogate modeling
LSExternalContext context = func.getExternalContext();
LSSurrogateParameters surrogateParams = context.enableSurrogateModeling();
// Sets lower bound
context.setLowerBound(0.0);
model.close();
// Parametrizes the solver
if (timeLimit != 0) {
localsolver.getParam().setTimeLimit(timeLimit);
}
// Sets the maximum number of evaluations
surrogateParams.setEvaluationLimit(evaluationLimit);
// Adds evaluation points
for (EvaluatedPoint evaluatedPoint : revenueManagement.getEvaluatedPoints()) {
LSEvaluationPoint evaluationPoint = surrogateParams.createEvaluationPoint();
for (int i = 0; i < nbPeriods; i++) {
evaluationPoint.addArgument((lsint) evaluatedPoint.getPoint(i));
}
evaluationPoint.setReturnValue(evaluatedPoint.getValue());
}
localsolver.solve();
}
// Writes the solution in a file
void writeSolution(const string& fileName) {
ofstream outfile;
outfile.exceptions(ofstream::failbit | ofstream::badbit);
outfile.open(fileName.c_str());
outfile << "obj=" << funcCall.getDoubleValue() << endl;
outfile << "b=" << variables[0].getIntValue() << endl;
for (unsigned int i = 1; i < variables.size(); i++) {
outfile << "r" << (i+1) << "=" << variables[i].getIntValue() << endl;
}
}
};
int main(int argc, char** argv) {
const char* solFile = argc > 1 ? argv[1] : NULL;
const char* strTimeLimit = argc > 2 ? argv[2] : "0";
const char* strEvaluationLimit = argc > 3 ? argv[3] : "30";
try {
RevenueManagement model;
model.solve(atoi(strTimeLimit), atoi(strEvaluationLimit));
if (solFile != NULL) model.writeSolution(solFile);
} catch (const exception& e) {
cerr << "An error occurred: " << e.what() << endl;
return 1;
}
return 0;
}
- Compilation / Execution (Windows)
- copy %LS_HOME%\bin\localsolvernet.dll .csc RevenueManagement.cs /reference:localsolvernet.dllRevenueManagement
/********** RevenueManagement.cs **********/
using System;
using System.IO;
using System.Collections.Generic;
using localsolver;
public class RevenueManagement : IDisposable
{
public class EvaluatedPoint
{
private int[] point;
private double value;
public EvaluatedPoint(int[] point, double value)
{
this.point = point;
this.value = value;
}
public int GetPoint(int index) {
return point[index];
}
public double GetValue() {
return value;
}
}
/* External function */
public class RevenueManagementFunction
{
private int seed;
private const int nbPeriods = 3;
private const int purchasePrice = 80;
private const int nbSimulations = (int) 1e6;
private readonly int[] prices = {100, 300, 400};
private readonly int[] meanDemands = {50, 20, 30};
private List<EvaluatedPoint> evaluatedPoints = new List<EvaluatedPoint>();
public RevenueManagementFunction(int seed)
{
this.seed = seed;
int[] point = {100, 50, 30};
evaluatedPoints.Add(new EvaluatedPoint(point, 4740.99));
}
public double Call(LSExternalArgumentValues argumentValues)
{
// Initial quantity purchased
int nbUnitsPurchased = (int) argumentValues.GetIntValue(0);
// Number of units that should be left for future periods
int[] nbUnitsReserved = new int[nbPeriods];
for (int j = 0; j < nbPeriods - 1; j++)
{
nbUnitsReserved[j] = (int) argumentValues.GetIntValue(j+1);
}
nbUnitsReserved[nbPeriods - 1] = 0;
// Sets seed for reproducibility
Random rng = new Random(seed);
// Creates distribution
double[] X = new double[nbSimulations];
for (int i = 0; i < nbSimulations; i++)
{
X[i] = GammaSample(rng);
}
double[,] Y = new double[nbSimulations, nbPeriods];
for (int i = 0; i < nbSimulations; i++)
{
for (int j = 0; j < nbPeriods; j++)
{
Y[i,j] = ExponentialSample(rng);
}
}
// Runs simulations
double sumProfit = 0;
for (int i = 0; i < nbSimulations; i++)
{
int remainingCapacity = nbUnitsPurchased;
for (int j = 0; j < nbPeriods; j++)
{
// Generates demand for period j
int demand = (int) (meanDemands[j] * X[i] * Y[i,j]);
int nbUnitsSold = Math.Min(Math.Max(remainingCapacity - nbUnitsReserved[j],
0), demand);
remainingCapacity = remainingCapacity - nbUnitsSold;
sumProfit += prices[j] * nbUnitsSold;
}
}
// Calculates mean revenue
double meanProfit = sumProfit / nbSimulations;
double meanRevenue = meanProfit - purchasePrice * nbUnitsPurchased;
return meanRevenue;
}
private static double ExponentialSample(Random rng, double rateParam = 1.0)
{
double u = rng.NextDouble();
return Math.Log(1 - u) / (-rateParam);
}
private static double GammaSample(Random rng, double scaleParam = 1.0)
{
return ExponentialSample(rng, scaleParam);
}
public int GetNbPeriods()
{
return nbPeriods;
}
public List<EvaluatedPoint> GetEvaluatedPoints() {
return evaluatedPoints;
}
}
// Solver
private LocalSolver localsolver;
// LS Program variables
private LSExpression[] variables;
private LSExpression funcCall;
public RevenueManagement()
{
localsolver = new LocalSolver();
}
public void Dispose()
{
if (localsolver != null)
localsolver.Dispose();
}
public void Solve(int timeLimit, int evaluationLimit)
{
// Declares the optimization model
LSModel model = localsolver.GetModel();
// Generates data
RevenueManagementFunction revenueManagement = new RevenueManagementFunction(1);
int nbPeriods = revenueManagement.GetNbPeriods();
// Declares decision variables
variables = new LSExpression[nbPeriods];
for (int i = 0; i < nbPeriods; i++)
{
variables[i] = model.Int(0, 100);
}
// Creates the function
LSDoubleExternalFunction func = new LSDoubleExternalFunction(revenueManagement.Call);
LSExpression funcExpr = model.DoubleExternalFunction(func);
// Calls function
funcCall = model.Call(funcExpr);
for (int i = 0; i < nbPeriods; i++)
{
funcCall.AddOperand(variables[i]);
}
// Declares constraints
for (int i = 1; i < nbPeriods; i++)
{
model.Constraint(variables[i] <= variables[i-1]);
}
// Maximizes function call
model.Maximize(funcCall);
// Enables surrogate modeling
LSExternalContext context = funcExpr.GetExternalContext();
LSSurrogateParameters surrogateParams = context.EnableSurrogateModeling();
// Sets lower bound
context.SetLowerBound(0.0);
model.Close();
// Parametrizes the solver
if (timeLimit != 0)
{
localsolver.GetParam().SetTimeLimit(timeLimit);
}
// Sets the maximum number of evaluations
surrogateParams.SetEvaluationLimit(evaluationLimit);
// Adds evaluation points
foreach (EvaluatedPoint evaluatedPoint in revenueManagement.GetEvaluatedPoints())
{
LSEvaluationPoint evaluationPoint = surrogateParams.CreateEvaluationPoint();
for (int i = 0; i < nbPeriods; i++)
{
evaluationPoint.AddArgument(evaluatedPoint.GetPoint(i));
}
evaluationPoint.SetReturnValue(evaluatedPoint.GetValue());
}
localsolver.Solve();
}
// Writes the solution in a file
public void WriteSolution(string fileName)
{
using (StreamWriter output = new StreamWriter(fileName))
{
output.WriteLine("obj=" + funcCall.GetDoubleValue());
output.WriteLine("b=" + variables[0].GetIntValue());
for (int i = 1; i < variables.Length; i++) {
output.WriteLine("r" + i + "=" + variables[i].GetIntValue());
}
}
}
public static void Main(string[] args)
{
string outputFile = args.Length > 0 ? args[0] : null;
string strTimeLimit = args.Length > 1 ? args[1] : "0";
string strEvaluationLimit = args.Length > 2 ? args[2] : "30";
using (RevenueManagement model = new RevenueManagement())
{
model.Solve(int.Parse(strTimeLimit), int.Parse(strEvaluationLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
}
- Compilation / Execution (Windows)
- javac RevenueManagement.java -cp %LS_HOME%\bin\localsolver.jarjava -cp %LS_HOME%\bin\localsolver.jar;. RevenueManagement
- Compilation / Execution (Linux)
- javac RevenueManagement.java -cp /opt/localsolver_11_0/bin/localsolver.jarjava -cp /opt/localsolver_11_0/bin/localsolver.jar:. RevenueManagement
/********** RevenueManagement.java **********/
import java.io.*;
import java.lang.Math;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import localsolver.*;
public class RevenueManagement {
private static class EvaluatedPoint {
private int[] point;
private double value;
public EvaluatedPoint(int[] point, double value) {
this.point = point;
this.value = value;
}
}
/* External function */
private static class RevenueManagementFunction implements LSDoubleExternalFunction {
private int seed;
private int nbPeriods = 3;
private int purchasePrice = 80;
private int nbSimulations = (int) 1e6;
private int[] prices = {100, 300, 400};
private int[] meanDemands = {50, 20, 30};
private List<EvaluatedPoint> evaluatedPoints = new ArrayList<EvaluatedPoint>();
public RevenueManagementFunction(int seed) {
this.seed = seed;
int[] point = {100, 50, 30};
evaluatedPoints.add(new EvaluatedPoint(point, 4740.99));
}
@Override
public double call(LSExternalArgumentValues argumentValues) {
// Initial quantity purchased
int nbUnitsPurchased = (int) argumentValues.getIntValue(0);
// Number of units that should be left for future periods
int[] nbUnitsReserved = new int[nbPeriods];
for (int j = 0; j < nbPeriods - 1; j++) {
nbUnitsReserved[j] = (int) argumentValues.getIntValue(j+1);
}
nbUnitsReserved[nbPeriods - 1] = 0;
// Sets seed for reproducibility
Random rng = new Random(seed);
// Creates distribution
double rateParam = 1.0;
double scaleParam = 1.0;
double[] X = new double[nbSimulations];
for (int i = 0; i < nbSimulations; i++) {
X[i] = gammaSample(rng, rateParam);
}
double[][] Y = new double[nbSimulations][nbPeriods];
for (int i = 0; i < nbSimulations; i++) {
for (int j = 0; j < nbPeriods; j++) {
Y[i][j] = exponentialSample(rng, scaleParam);
}
}
// Runs simulations
double sumProfit = 0;
for (int i = 0; i < nbSimulations; i++) {
int remainingCapacity = nbUnitsPurchased;
for (int j = 0; j < nbPeriods; j++) {
// Generates demand for period j
int demand = (int) (meanDemands[j] * X[i] * Y[i][j]);
int nbUnitsSold = Math.min(Math.max(remainingCapacity - nbUnitsReserved[j],
0), demand);
remainingCapacity = remainingCapacity - nbUnitsSold;
sumProfit += prices[j] * nbUnitsSold;
}
}
// Calculates mean revenue
double meanProfit = sumProfit / nbSimulations;
double meanRevenue = meanProfit - purchasePrice * nbUnitsPurchased;
return meanRevenue;
}
private static double exponentialSample(Random rng, double rateParam) {
double u = rng.nextDouble();
return Math.log(1 - u) / (-rateParam);
}
private static double gammaSample(Random rng, double scaleParam) {
return exponentialSample(rng, scaleParam);
}
}
// Solver
private final LocalSolver localsolver;
// LS Program variables
private LSExpression[] variables;
private LSExpression funcCall;
private RevenueManagement(LocalSolver localsolver) {
this.localsolver = localsolver;
}
// Declares the optimization model
private void solve(int timeLimit, int evaluationLimit) {
LSModel model = localsolver.getModel();
// Generates data
RevenueManagementFunction revenueManagement = new RevenueManagementFunction(1);
int nbPeriods = revenueManagement.nbPeriods;
// Declares decision variables
variables = new LSExpression[nbPeriods];
for (int i = 0; i < nbPeriods; i++) {
variables[i] = model.intVar(0, 100);
}
// Creates the function
LSExpression func = model.doubleExternalFunction(revenueManagement);
// Calls function with operands
funcCall = model.call(func);
for (int i = 0; i < nbPeriods; i++) {
funcCall.addOperand(variables[i]);
}
// Declares constraints
for (int i = 1; i < nbPeriods; i++) {
model.constraint(model.leq(variables[i], variables[i-1]));
}
// Maximizes function call
model.maximize(funcCall);
// Enables surrogate modeling
LSExternalContext context = func.getExternalContext();
LSSurrogateParameters surrogateParams = context.enableSurrogateModeling();
// Sets lower bound
context.setLowerBound(0.0);
model.close();
// Parametrizes the solver
if (timeLimit != 0) {
localsolver.getParam().setTimeLimit(timeLimit);
}
// Sets the maximum number of evaluations
surrogateParams.setEvaluationLimit(evaluationLimit);
// Adds evaluation points
for (EvaluatedPoint evaluatedPoint : revenueManagement.evaluatedPoints) {
LSEvaluationPoint evaluationPoint = surrogateParams.createEvaluationPoint();
for (int i = 0; i < nbPeriods; i++) {
evaluationPoint.addArgument(evaluatedPoint.point[i]);
}
evaluationPoint.setReturnValue(evaluatedPoint.value);
}
localsolver.solve();
}
// Writes the solution in a file
private void writeSolution(String fileName) throws IOException {
try (PrintWriter output = new PrintWriter(fileName)) {
output.println("obj=" + funcCall.getDoubleValue());
output.println("b=" + variables[0].getIntValue());
for (int i = 1; i < variables.length; i++) {
output.println("r" + i + "=" + variables[i].getIntValue());
}
}
}
public static void main(String[] args) {
String outputFile = args.length > 0 ? args[0] : null;
String strTimeLimit = args.length > 1 ? args[1] : "0";
String strEvaluationLimit = args.length > 2 ? args[2] : "30";
try (LocalSolver localsolver = new LocalSolver()) {
RevenueManagement model = new RevenueManagement(localsolver);
model.solve(Integer.parseInt(strTimeLimit), Integer.parseInt(strEvaluationLimit));
if (outputFile != null) {
model.writeSolution(outputFile);
}
} catch (Exception ex) {
System.err.println(ex);
ex.printStackTrace();
System.exit(1);
}
}
}