Bin Packing with Conflicts (BPPC)

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

  • Use the intersection operator

Problem

../_images/binpacking.svg

In the bin packing with conflicts problem, a number of items with known weights must be assigned to bins with uniform capacity. Furthermore, each item is in conflict with a list of forbidden items in the same bin. The objective is to minimize the number of bins used such that all items are placed while respecting the items in conflict. It is an NP-hard problem because it generalizes both the bin packing problem (BPP) and the vertex coloring problem (VCP).

Download the example




Data

The instances provided are the Muritiba instances from the BPPLIB. The format of the data files is as follows:

  • First line: number of items and capacity of a bin

  • From the second line, for each item:
    • item identifier

    • item weight

    • items in conflict with that 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 intersection() operator takes in argument two operands that can be either an array or a collection, and returns an unordered set composed of the values present in both operands. Here, we use this operator to retrieve for each item the intersection between its forbidden list of items and the list of items in its bin. Thus, compeling the count of this intersection to be equal to 0 allows to respect the constraints of conflict.

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_conflicts.hxm inFileName=instances/BPPC_1_6_8.txt [hxTimeLimit=] [solFileName=]
use io;

/* Read instance data */
function input() {
    local usage = "Usage: hexaly bin_packing_conflict.hxm "
            + "inFileName=inputFile [solFileName=outputFile] [hxTimeLimit=timeLimit]";

    if (inFileName == nil) throw usage;
    local inFile = io.openRead(inFileName);

    nbItems = inFile.readInt();
    binCapacity = inFile.readInt();

    for [i in 0...nbItems] {
        line = inFile.readln();
        local elements = line.trim().split();
        itemWeights[i] = elements[1].toInt();
        for [j in 2...elements.count()] {
            forbiddenItems[i][j-2] = elements[j].toInt();
        }
        if (elements.count() == 2) {
            forbiddenItems[i] = {};
        }
    }

    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);

    // Find the bin where an item is packed
    for [i in 0...nbItems] {
        binForItem[i] <- find(bins, i);
    }

    // Each item must be in one bin and one bin only
    constraint partition[k in 0...nbMaxBins](bins[k]);

    // Forbidden constraints
    for [i in 0...nbItems] {
        itemsIntersection  <- intersection(bins[binForItem[i]], forbiddenItems[i]);
        constraint count(itemsIntersection) == 0;
    }
    
    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
    hxObjectiveThreshold = 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\python
python bin_packing_conflicts.py instances\BPPC_1_6_8.txt
Execution (Linux)
export PYTHONPATH=/opt/hexaly_13_5/bin/python
python bin_packing_conflicts.py instances/BPPC_1_6_8.txt
import hexaly.optimizer
import sys
import math

if len(sys.argv) < 2:
    print("Usage: python bin_packing_conflicts.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
    filename = sys.argv[1]
    count = 0
    weights_data = []
    forbidden_items = []
    with open(filename) as f:
        for line in f:
            line = line.split()
            if count == 0:
                nb_items = int(line[0])
                bin_capacity = int(line[1])
            else:
                weights_data.append(int(line[1]))
                forbidden_items.append([])
                for i in range(2, len(line)):
                    forbidden_items[count-1].append(int(line[i]))
            count += 1
                    
    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: bins[k] represents the items in bin k
    bins = [model.set(nb_items) for _ in range(nb_max_bins)]

    # Transform bins and itemFordbidden list into hx expression
    bins_array = model.array(bins)
    forbidden_items_array = model.array(forbidden_items)

    # Find the bin where an item is packed
    bin_for_item = [model.find(bins_array, i) for i in range(nb_items)]

    # 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])

    # Forbidden constraint for each items
    for i in range(nb_items):
        items_intersection = model.intersection(forbidden_items_array[i], bins_array[bin_for_item[i]])
        model.constraint(model.count(items_intersection) == 0)

    # 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_items):
                f.write("item:%d Weight:%d" % (k, weights_data[k]))
                f.write("\n")
            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_conflicts.cpp -I%HX_HOME%\include /link %HX_HOME%\bin\hexaly135.lib
bin_packing_conflicts instances\BPPC_1_6_8.txt
Compilation / Execution (Linux)
g++ bin_packing_conflicts.cpp -I/opt/hexaly_13_5/include -lhexaly135 -lpthread -o bin_packing_conflicts
./bin_packing_conflicts instances/BPPC_1_6_8.txt
#include "optimizer/hexalyoptimizer.h"
#include <cmath>
#include <fstream>
#include <iostream>
#include <numeric>
#include <vector>

using namespace hexaly;
using namespace std;

class BinPackingConflicts {
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;

    // List of forbidden items
    std::vector<std::vector<int>> forbiddenItems;

    // Hexaly Optimizer
    HexalyOptimizer optimizer;

    // Decision variables
    std::vector<HxExpression> bins;

    // Bin where the item is
    std::vector<HxExpression> binForItem;

    // 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) {
        int count = 0;
        ifstream infile(fileName);
        infile >> nbItems;
        infile >> binCapacity;
        weightsData.resize(nbItems);
        infile.ignore(numeric_limits<streamsize>::max(), '\n');
        string line;
        while (getline(infile, line)) {
            istringstream ss(line);
            std::vector<string> lineParts;
            string part;
            while (ss >> part) {
                lineParts.push_back(part);
            }
            weightsData[count] = stoi(lineParts[1]);
            forbiddenItems.push_back(std::vector<int>());
            for (size_t i = 2; i < lineParts.size(); ++i) {
                forbiddenItems[count].push_back(stoi(lineParts[i]));
            }
            ++count;
        }
        infile.close();

        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);
        HxExpression binsArray = model.array();
        HxExpression forbiddenItemsArray = model.array();

        // Set decisions: bins[k] represents the items in bin k
        for (int k = 0; k < nbMaxBins; ++k) {
            bins[k] = model.setVar(nbItems);
            binsArray.addOperand(bins[k]);
        }

        for (int i = 0; i < nbItems; ++i) {
            forbiddenItemsArray.addOperand(model.array(forbiddenItems[i].begin(), forbiddenItems[i].end()));
        }

        // Find the bin where an item is packed
        binForItem.resize(nbItems);
        for (int i = 0; i < nbItems; ++i) {
            binForItem[i] = model.find(binsArray, i);
        }

        // 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;
        }

        // Forbidden constraint for each items
        for (int i = 0; i < nbItems; ++i) {
            HxExpression itemsIntersection = model.intersection(binsArray[binForItem[i]], forbiddenItemsArray[i]);
            model.constraint(model.count(itemsIntersection) == 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 {
        BinPackingConflicts 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 BinPackingConflicts.cs /reference:Hexaly.NET.dll
BinPackingConflicts instances\BPPC_1_6_8.txt
using System;
using System.IO;
using System.Linq;
using System.Collections.Generic;
using Hexaly.Optimizer;

public class BinPackingConflicts : 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;

    // List of forbidden items
    private List<List<int>> forbiddenItems;

    // Hexaly Optimizer
    HexalyOptimizer optimizer;

    // Decision variables
    HxExpression[] bins;

    // Bin where the item is
    private HxExpression[] binForItem;

    // Weight of each bin in the solution
    HxExpression[] binWeights;

    // Whether the bin is used in the solution
    HxExpression[] binsUsed;

    // Objective
    HxExpression totalBinsUsed;

    public BinPackingConflicts()
    {
        optimizer = new HexalyOptimizer();
    }

    /* Read instance data */
    void ReadInstance(string fileName)
    {
        int count = 0;
            using (StreamReader input = new StreamReader(fileName))
            {
                forbiddenItems = new List<List<int>>();
                string firstLine = input.ReadLine();
                string[] firstLineParts = firstLine.Split(' ');
                nbItems = int.Parse(firstLineParts[0]);
                binCapacity = int.Parse(firstLineParts[1]);
                weightsData = new long[nbItems];

                string line;
                while ((line = input.ReadLine()) != null)
                {
                    string[] lineParts = line.Split(new[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
                    weightsData[count] = long.Parse(lineParts[1]);
                    forbiddenItems.Add(new List<int>());
                    for (int i = 2; i < lineParts.Length; ++i)
                    {
                        forbiddenItems[count].Add(int.Parse(lineParts[i]));
                    }
                    count++;
                }
            }
        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];
        HxExpression binsArray = model.Array();
        HxExpression forbiddenItemsArray = model.Array();

        // Set decisions: bins[k] represents the items in bin k
        for (int k = 0; k < nbMaxBins; ++k)
        {
            bins[k] = model.Set(nbItems);
            binsArray.AddOperand(bins[k]);
        }

        for (int i = 0; i < nbItems; ++i)
        {
            forbiddenItemsArray.AddOperand(model.Array(forbiddenItems[i]));
        }

        // Find the bin where an item is packed
        binForItem = new HxExpression[nbItems];
        for (int i = 0; i < nbItems; ++i) {
            binForItem[i] = model.Find(binsArray, i);
        }

        // 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;
        }

        // Forbidden constraint for each items
        for (int i = 0; i < nbItems; ++i)
        {
            HxExpression itemsIntersection = model.Intersection(binsArray[binForItem[i]], forbiddenItemsArray[i]);
            model.Constraint(model.Count(itemsIntersection) == 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: BinPackingConflicts 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 (BinPackingConflicts model = new BinPackingConflicts())
        {
            model.ReadInstance(instanceFile);
            model.Solve(int.Parse(strTimeLimit));
            if (outputFile != null)
                model.WriteSolution(outputFile);
        }
    }
}
Compilation / Execution (Windows)
javac BinPackingConflicts.java -cp %HX_HOME%\bin\hexaly.jar
java -cp %HX_HOME%\bin\hexaly.jar;. BinPackingConflicts instances\BPPC_1_6_8.txt
Compilation / Execution (Linux)
javac BinPackingConflicts.java -cp /opt/hexaly_13_5/bin/hexaly.jar
java -cp /opt/hexaly_13_5/bin/hexaly.jar:. BinPackingConflicts instances/BPPC_1_6_8.txt
import java.util.*;
import java.io.*;
import com.hexaly.optimizer.*;

public class BinPackingConflicts {
    // 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;

    // List of forbidden items
    private List<List<Integer>> forbiddenItems;

    // Hexaly Optimizer
    private final HexalyOptimizer optimizer;

    // Decision variables
    private HxExpression[] bins;

    // Bin where the item is
    private HxExpression[] binForItem;

    // 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 BinPackingConflicts(HexalyOptimizer optimizer) {
        this.optimizer = optimizer;
    }

    /* Read instance data */
    private void readInstance(String fileName) throws IOException {
        int count = 0;
        try (Scanner input = new Scanner(new File(fileName))) {
            nbItems = input.nextInt();
            binCapacity = input.nextInt();
            weightsData = new long[nbItems];
            forbiddenItems = new ArrayList<>();
            input.nextLine();
            while (input.hasNextLine()) {
                String line = input.nextLine();
                String[] lineParts = line.split("\\s+");
                weightsData[count] = Integer.parseInt(lineParts[1]);
                forbiddenItems.add(new ArrayList<>());
                for (int i = 2; i < lineParts.length; ++i) {
                    forbiddenItems.get(count).add(Integer.parseInt(lineParts[i]));
                }
                count++;
            }

            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];
        HxExpression binsArray = model.array();
        HxExpression forbiddenItemsArray = model.array();

        // Set decisions: bins[k] represents the items in bin k
        for (int k = 0; k < nbMaxBins; ++k) {
            bins[k] = model.setVar(nbItems);
            binsArray.addOperand(bins[k]);
        }

        for (int i = 0; i < nbItems; ++i) {
            forbiddenItemsArray.addOperand(model.array(forbiddenItems.get(i)));
        }

        // Find the bin where an item is packed
        binForItem = new HxExpression[nbItems];
        for (int i = 0; i < nbItems; ++i) {
            binForItem[i] = model.find(binsArray, i);
        }

        // 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);
        }

        // Forbidden constraint for each items
        for (int i = 0; i < nbItems; ++i) {
            HxExpression itemsIntersection = model.intersection(model.at(binsArray, binForItem[i]),
                    model.at(forbiddenItemsArray, i));
            model.constraint(model.eq(model.count(itemsIntersection), 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 BinPackingConflicts 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()) {
            BinPackingConflicts model = new BinPackingConflicts(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);
        }
    }
}