Bin Packing (BPP)¶
Principles learned¶
Create a set decision variable
Use a lambda expression to compute a sum on a set
Specify a threshold to stop the search after a target is reached
Problem¶
In the bin packing problem, a number of items with known weights must be assigned to bins with uniform capacity. The objective is to minimize the number of bins used such that all items are placed. It is a typical example of an NP-hard problem.
Download the exampleData¶
The instances provided are the Falkenauer instances from the BPPLIB. The format of the data files is as follows:
First line: number of items
Second line: capacity of a bin
The weight for each item
Program¶
The model implemented here makes use of set variables. For each bin we define a set which describes the items assigned to that bin. Those sets are constrained to form a partition, which means that an item must be assigned to exactly one bin.
For each bin, the combined weight of the items must be smaller than its capacity. This weight is computed directly using the sum operator on the set: we define a function that takes an item index and returns the associated weight. See our documentation on this topic for details.
The model computes simple lower and upper bounds on the optimal number
of bins. It only defines nbMaxBins
set variables, and uses
hxObjectiveThreshold
to stop the search if a solution with nbMinBins
bins is reached.
- Execution:
- hexaly bin_packing.hxm inFileName=instances/t60_00.txt [hxTimeLimit=] [solFileName=]
use io;
/* Read instance data */
function input() {
local usage = "Usage: hexaly bin_packing.hxm "
+ "inFileName=inputFile [solFileName=outputFile] [hxTimeLimit=timeLimit]";
if (inFileName == nil) throw usage;
local inFile = io.openRead(inFileName);
nbItems = inFile.readInt();
binCapacity = inFile.readInt();
itemWeights[i in 0...nbItems] = inFile.readInt();
nbMinBins = ceil(sum[i in 0...nbItems](itemWeights[i]) / binCapacity);
nbMaxBins = min(nbItems, 2 * nbMinBins);
}
/* Declare the optimization model */
function model() {
// Set decisions: bins[k] represents the items in bin k
bins[k in 0...nbMaxBins] <- set(nbItems);
// Each item must be in one bin and one bin only
constraint partition[k in 0...nbMaxBins](bins[k]);
for [k in 0...nbMaxBins] {
// Weight constraint for each bin
binWeights[k] <- sum(bins[k], i => itemWeights[i]);
constraint binWeights[k] <= binCapacity;
// Bin k is used if at least one item is in it
binsUsed[k] <- (count(bins[k]) > 0);
}
// Count the used bins
totalBinsUsed <- sum[k in 0...nbMaxBins](binsUsed[k]);
// Minimize the number of used bins
minimize totalBinsUsed;
}
/* Parametrize the solver */
function param() {
if (hxTimeLimit == nil) hxTimeLimit = 5;
// Stop the search if the lower threshold is reached
lsObjectiveThreshold = nbMinBins;
}
/* Write the solution in a file */
function output() {
if (solFileName == nil) return;
local solFile = io.openWrite(solFileName);
for [k in 0...nbMaxBins] {
if (bins[k].value.count() == 0) continue;
solFile.print("Bin weight: ", binWeights[k].value, " | Items: ");
for [e in bins[k].value]
solFile.print(e + " ");
solFile.println();
}
}
- Execution (Windows)
- set PYTHONPATH=%HX_HOME%\bin\pythonpython bin_packing.py instances\t60_00.txt
- Execution (Linux)
- export PYTHONPATH=/opt/hexaly_13_0/bin/pythonpython bin_packing.py instances/t60_00.txt
import hexaly.optimizer
import sys
import math
if len(sys.argv) < 2:
print("Usage: python bin_packing.py inputFile [outputFile] [timeLimit]")
sys.exit(1)
def read_integers(filename):
with open(filename) as f:
return [int(elem) for elem in f.read().split()]
with hexaly.optimizer.HexalyOptimizer() as optimizer:
# Read instance data
file_it = iter(read_integers(sys.argv[1]))
nb_items = int(next(file_it))
bin_capacity = int(next(file_it))
weights_data = [int(next(file_it)) for i in range(nb_items)]
nb_min_bins = int(math.ceil(sum(weights_data) / float(bin_capacity)))
nb_max_bins = min(nb_items, 2 * nb_min_bins)
#
# Declare the optimization model
#
model = optimizer.model
# Set decisions: bin[k] represents the items in bin k
bins = [model.set(nb_items) for _ in range(nb_max_bins)]
# Each item must be in one bin and one bin only
model.constraint(model.partition(bins))
# Create an array and a function to retrieve the item's weight
weights = model.array(weights_data)
weight_lambda = model.lambda_function(lambda i: weights[i])
# Weight constraint for each bin
bin_weights = [model.sum(b, weight_lambda) for b in bins]
for w in bin_weights:
model.constraint(w <= bin_capacity)
# Bin k is used if at least one item is in it
bins_used = [model.count(b) > 0 for b in bins]
# Count the used bins
total_bins_used = model.sum(bins_used)
# Minimize the number of used bins
model.minimize(total_bins_used)
model.close()
# Parameterize the optimizer
if len(sys.argv) >= 4:
optimizer.param.time_limit = int(sys.argv[3])
else:
optimizer.param.time_limit = 5
# Stop the search if the lower threshold is reached
optimizer.param.set_objective_threshold(0, nb_min_bins)
optimizer.solve()
# Write the solution in a file
if len(sys.argv) >= 3:
with open(sys.argv[2], 'w') as f:
for k in range(nb_max_bins):
if bins_used[k].value:
f.write("Bin weight: %d | Items: " % bin_weights[k].value)
for e in bins[k].value:
f.write("%d " % e)
f.write("\n")
- Compilation / Execution (Windows)
- cl /EHsc bin_packing.cpp -I%HX_HOME%\include /link %HX_HOME%\bin\hexaly130.libbin_packing instances\t60_00.txt
- Compilation / Execution (Linux)
- g++ bin_packing.cpp -I/opt/hexaly_13_0/include -lhexaly130 -lpthread -o bin_packing./bin_packing instances/t60_00.txt
#include "optimizer/hexalyoptimizer.h"
#include <cmath>
#include <fstream>
#include <iostream>
#include <numeric>
#include <vector>
using namespace hexaly;
using namespace std;
class BinPacking {
private:
// Number of items
int nbItems;
// Capacity of each bin
int binCapacity;
// Maximum number of bins
int nbMaxBins;
// Minimum number of bins
int nbMinBins;
// Weight of each item
std::vector<hxint> weightsData;
// Hexaly Optimizer
HexalyOptimizer optimizer;
// Decision variables
std::vector<HxExpression> bins;
// Weight of each bin in the solution
std::vector<HxExpression> binWeights;
// Whether the bin is used in the solution
std::vector<HxExpression> binsUsed;
// Objective
HxExpression totalBinsUsed;
public:
/* Read instance data */
void readInstance(const string& fileName) {
ifstream infile;
infile.exceptions(ifstream::failbit | ifstream::badbit);
infile.open(fileName.c_str());
infile >> nbItems;
infile >> binCapacity;
weightsData.resize(nbItems);
for (int i = 0; i < nbItems; ++i) {
infile >> weightsData[i];
}
nbMinBins = ceil(accumulate(weightsData.begin(), weightsData.end(), 0.0) / binCapacity);
nbMaxBins = min(2 * nbMinBins, nbItems);
}
void solve(int limit) {
// Declare the optimization model
HxModel model = optimizer.getModel();
bins.resize(nbMaxBins);
binWeights.resize(nbMaxBins);
binsUsed.resize(nbMaxBins);
// Set decisions: bins[k] represents the items in bin k
for (int k = 0; k < nbMaxBins; ++k) {
bins[k] = model.setVar(nbItems);
}
// Each item must be in one bin and one bin only
model.constraint(model.partition(bins.begin(), bins.end()));
// Create an array and a function to retrieve the item's weight
HxExpression weights = model.array(weightsData.begin(), weightsData.end());
HxExpression weightLambda = model.createLambdaFunction([&](HxExpression i) { return weights[i]; });
for (int k = 0; k < nbMaxBins; ++k) {
// Weight constraint for each bin
binWeights[k] = model.sum(bins[k], weightLambda);
model.constraint(binWeights[k] <= binCapacity);
// Bin k is used if at least one item is in it
binsUsed[k] = model.count(bins[k]) > 0;
}
// Count the used bins
totalBinsUsed = model.sum(binsUsed.begin(), binsUsed.end());
// Minimize the number of used bins
model.minimize(totalBinsUsed);
model.close();
// Parametrize the optimizer
optimizer.getParam().setTimeLimit(limit);
// Stop the search if the lower threshold is reached
optimizer.getParam().setObjectiveThreshold(0, (hxint)nbMinBins);
optimizer.solve();
}
/* Write the solution in a file */
void writeSolution(const string& fileName) {
ofstream outfile;
outfile.exceptions(ofstream::failbit | ofstream::badbit);
outfile.open(fileName.c_str());
for (int k = 0; k < nbMaxBins; ++k) {
if (binsUsed[k].getValue()) {
outfile << "Bin weight: " << binWeights[k].getValue() << " | Items: ";
HxCollection binCollection = bins[k].getCollectionValue();
for (int i = 0; i < binCollection.count(); ++i) {
outfile << binCollection[i] << " ";
}
outfile << endl;
}
}
}
};
int main(int argc, char** argv) {
if (argc < 2) {
cerr << "Usage: bin_packing inputFile [outputFile] [timeLimit]" << endl;
return 1;
}
const char* instanceFile = argv[1];
const char* solFile = argc > 2 ? argv[2] : NULL;
const char* strTimeLimit = argc > 3 ? argv[3] : "5";
try {
BinPacking model;
model.readInstance(instanceFile);
model.solve(atoi(strTimeLimit));
if (solFile != NULL)
model.writeSolution(solFile);
return 0;
} catch (const exception& e) {
cerr << "An error occurred: " << e.what() << endl;
return 1;
}
}
- Compilation / Execution (Windows)
- copy %HX_HOME%\bin\Hexaly.NET.dll .csc BinPacking.cs /reference:Hexaly.NET.dllBinPacking instances\t60_00.txt
using System;
using System.IO;
using System.Linq;
using Hexaly.Optimizer;
public class BinPacking : IDisposable
{
// Number of items
int nbItems;
// Capacity of each bin
int binCapacity;
// Maximum number of bins
int nbMaxBins;
// Minimum number of bins
int nbMinBins;
// Weight of each item
long[] weightsData;
// Hexaly Optimizer
HexalyOptimizer optimizer;
// Decision variables
HxExpression[] bins;
// Weight of each bin in the solution
HxExpression[] binWeights;
// Whether the bin is used in the solution
HxExpression[] binsUsed;
// Objective
HxExpression totalBinsUsed;
public BinPacking()
{
optimizer = new HexalyOptimizer();
}
/* Read instance data */
void ReadInstance(string fileName)
{
using (StreamReader input = new StreamReader(fileName))
{
nbItems = int.Parse(input.ReadLine());
binCapacity = int.Parse(input.ReadLine());
weightsData = new long[nbItems];
for (int i = 0; i < nbItems; ++i)
weightsData[i] = int.Parse(input.ReadLine());
nbMinBins = (int)Math.Ceiling((double)weightsData.Sum() / binCapacity);
nbMaxBins = Math.Min(2 * nbMinBins, nbItems);
}
}
public void Dispose()
{
if (optimizer != null)
optimizer.Dispose();
}
void Solve(int limit)
{
// Declare the optimization model
HxModel model = optimizer.GetModel();
bins = new HxExpression[nbMaxBins];
binWeights = new HxExpression[nbMaxBins];
binsUsed = new HxExpression[nbMaxBins];
// Set decisions: bin[k] represents the items in bin k
for (int k = 0; k < nbMaxBins; ++k)
bins[k] = model.Set(nbItems);
// Each item must be in one bin and one bin only
model.Constraint(model.Partition(bins));
// Create an array and a function to retrieve the item's weight
HxExpression weights = model.Array(weightsData);
HxExpression weightLambda = model.LambdaFunction(i => weights[i]);
for (int k = 0; k < nbMaxBins; ++k)
{
// Weight constraint for each bin
binWeights[k] = model.Sum(bins[k], weightLambda);
model.Constraint(binWeights[k] <= binCapacity);
// Bin k is used if at least one item is in it
binsUsed[k] = model.Count(bins[k]) > 0;
}
// Count the used bins
totalBinsUsed = model.Sum(binsUsed);
// Minimize the number of used bins
model.Minimize(totalBinsUsed);
model.Close();
// Parametrize the optimizer
optimizer.GetParam().SetTimeLimit(limit);
// Stop the search if the lower threshold is reached
optimizer.GetParam().SetObjectiveThreshold(0, nbMinBins);
optimizer.Solve();
}
/* Write the solution in a file */
void WriteSolution(string fileName)
{
using (StreamWriter output = new StreamWriter(fileName))
{
for (int k = 0; k < nbMaxBins; ++k)
{
if (binsUsed[k].GetValue() != 0)
{
output.Write("Bin weight: " + binWeights[k].GetValue() + " | Items: ");
HxCollection binCollection = bins[k].GetCollectionValue();
for (int i = 0; i < binCollection.Count(); ++i)
output.Write(binCollection[i] + " ");
output.WriteLine();
}
}
}
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("Usage: BinPacking inputFile [solFile] [timeLimit]");
Environment.Exit(1);
}
string instanceFile = args[0];
string outputFile = args.Length > 1 ? args[1] : null;
string strTimeLimit = args.Length > 2 ? args[2] : "5";
using (BinPacking model = new BinPacking())
{
model.ReadInstance(instanceFile);
model.Solve(int.Parse(strTimeLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
}
- Compilation / Execution (Windows)
- javac BinPacking.java -cp %HX_HOME%\bin\hexaly.jarjava -cp %HX_HOME%\bin\hexaly.jar;. BinPacking instances\t60_00.txt
- Compilation / Execution (Linux)
- javac BinPacking.java -cp /opt/hexaly_13_0/bin/hexaly.jarjava -cp /opt/hexaly_13_0/bin/hexaly.jar:. BinPacking instances/t60_00.txt
import java.util.*;
import java.io.*;
import com.hexaly.optimizer.*;
public class BinPacking {
// Number of items
private int nbItems;
// Capacity of each bin
private int binCapacity;
// Maximum number of bins
private int nbMaxBins;
// Minimum number of bins
private int nbMinBins;
// Weight of each item
private long[] weightsData;
// Hexaly Optimizer
private final HexalyOptimizer optimizer;
// Decision variables
private HxExpression[] bins;
// Weight of each bin in the solution
private HxExpression[] binWeights;
// Whether the bin is used in the solution
private HxExpression[] binsUsed;
// Objective
private HxExpression totalBinsUsed;
private BinPacking(HexalyOptimizer optimizer) {
this.optimizer = optimizer;
}
/* Read instance data */
private void readInstance(String fileName) throws IOException {
try (Scanner input = new Scanner(new File(fileName))) {
nbItems = input.nextInt();
binCapacity = input.nextInt();
weightsData = new long[nbItems];
for (int i = 0; i < nbItems; ++i) {
weightsData[i] = input.nextInt();
}
long sumWeights = 0;
for (int i = 0; i < nbItems; ++i) {
sumWeights += weightsData[i];
}
nbMinBins = (int) Math.ceil((double) sumWeights / binCapacity);
nbMaxBins = Math.min(2 * nbMinBins, nbItems);
}
}
private void solve(int limit) {
// Declare the optimization model
HxModel model = optimizer.getModel();
bins = new HxExpression[nbMaxBins];
binWeights = new HxExpression[nbMaxBins];
binsUsed = new HxExpression[nbMaxBins];
// Set decisions: bins[k] represents the items in bin k
for (int k = 0; k < nbMaxBins; ++k) {
bins[k] = model.setVar(nbItems);
}
// Each item must be in one bin and one bin only
model.constraint(model.partition(bins));
// Create an array and a lambda function to retrieve the item's weight
HxExpression weights = model.array(weightsData);
HxExpression weightLambda = model.lambdaFunction(i -> model.at(weights, i));
for (int k = 0; k < nbMaxBins; ++k) {
// Weight constraint for each bin
binWeights[k] = model.sum(bins[k], weightLambda);
model.constraint(model.leq(binWeights[k], binCapacity));
// Bin k is used if at least one item is in it
binsUsed[k] = model.gt(model.count(bins[k]), 0);
}
// Count the used bins
totalBinsUsed = model.sum(binsUsed);
// Minimize the number of used bins
model.minimize(totalBinsUsed);
model.close();
// Parametrize the optimizer
optimizer.getParam().setTimeLimit(limit);
// Stop the search if the lower threshold is reached
optimizer.getParam().setObjectiveThreshold(0, nbMinBins);
optimizer.solve();
}
/* Write the solution in a file */
private void writeSolution(String fileName) throws IOException {
try (PrintWriter output = new PrintWriter(fileName)) {
for (int k = 0; k < nbMaxBins; ++k) {
if (binsUsed[k].getValue() != 0) {
output.print("Bin weight: " + binWeights[k].getValue() + " | Items: ");
HxCollection binCollection = bins[k].getCollectionValue();
for (int i = 0; i < binCollection.count(); ++i) {
output.print(binCollection.get(i) + " ");
}
output.println();
}
}
}
}
public static void main(String[] args) {
if (args.length < 1) {
System.err.println("Usage: java BinPacking inputFile [outputFile] [timeLimit]");
System.exit(1);
}
String instanceFile = args[0];
String outputFile = args.length > 1 ? args[1] : null;
String strTimeLimit = args.length > 2 ? args[2] : "5";
try (HexalyOptimizer optimizer = new HexalyOptimizer()) {
BinPacking model = new BinPacking(optimizer);
model.readInstance(instanceFile);
model.solve(Integer.parseInt(strTimeLimit));
if (outputFile != null) {
model.writeSolution(outputFile);
}
} catch (Exception ex) {
System.err.println(ex);
ex.printStackTrace();
System.exit(1);
}
}
}