Job Shop (JSP)

Principles learned

  • Add multiple list decision variables

  • Constrain the number of elements in a list

  • Use interval decision variables

  • Order interval decision variables by pairing them up with a list variable

Problem

../_images/jobshop.svg

A set of jobs has to be processed on every machine of the shop. Each job consists in an ordered sequence of tasks (called activities), each representing the processing of the job on one of the machines. Each job has one activity per machine, and cannot start an activity while the previous activity of the job is not completed. Each activity has a given processing time and each machine can only process one activity at a time.

The goal is to find a sequence of jobs that minimizes the makespan: the time when all jobs have been processed.

Download the example


Data

The instances provided follow the Taillard format. The format of the data files is as follows:

  • First line: number of jobs, number of machines, seed used to generate the instance, upper and lower bound previously found.

  • For each job: the processing time on each machine (given in the processing order).

  • For each job: the processing order (ordered list of visited machines).

Program

We use interval decision variables to model the time ranges of the activities. The length of the interval is constrained by the processing time of each activity.

The precedence constraints are easily written: for each job, any activity of this job must be placed after the activity processed by the previous machine.

In addition to the interval decisions representing the time ranges of the activities, we also use list decision variables. As in the Flowshop example, a list models the ordering of activities within a machine. We constrain all the jobs to be processed on each machine thanks to the “count” operator.

The disjunctive resource constraints — each machine can only process one activity at a time — can be reformulated as follows: given a sequence of jobs, the activity corresponding to any job must be placed after the activity corresponding to the previous job.

To model these constraints, we pair up the interval decisions (the time ranges) with the list decisions (the job orderings). We write a lambda function, expressing the relationship between the two consecutive activities. This function is used within an and operator over all activities processed by a machine.

The makespan to minimize is the time when all the activities have been processed.

If you are interested in a more general case, where the activities are not ordered on each machine, you can now study our openshop model.

Execution:
localsolver jobshop.lsp inFileName=instances/ft10.txt [outFileName=] [lsTimeLimit=]
use io;

/* Read instance data. The input files follow the "Taillard" format */
function input() {
    local usage = "Usage: localsolver jobshop.lsp inFileName=instanceFile "
            + "[outFileName=outputFile] [lsTimeLimit=timeLimit]";
    if (inFileName == nil) throw usage;

    inFile = io.openRead(inFileName);
    inFile.readln();
    nbJobs = inFile.readInt();
    nbMachines = inFile.readInt();
    inFile.readln();
    inFile.readln();
    // Processing times for each job on each machine (given in the processing order)
    processingTimesInProcessingOrder[j in 0...nbJobs][m in 0...nbMachines] = inFile.readInt();
    inFile.readln();
    for [j in 0...nbJobs][k in 0...nbMachines] {
        local m = inFile.readInt() - 1;
        // Processing order of machines for each job
        machineOrder[j][k] = m;
        // Reorder processing times: processingTime[j][m] is the processing time of the
        // task of job j that is processed on machine m
        processingTime[j][m] = processingTimesInProcessingOrder[j][k];
    }
    inFile.close();

    // Trivial upper bound for the start times of the tasks
    maxStart = sum[j in 0...nbJobs][m in 0...nbMachines] (processingTime[j][m]);
}

/* Declare the optimization model */
function model() {
    // Interval decisions: time range of each task
    // tasks[j][m] is the interval of time of the task of job j which is processed
    // on machine m
    tasks[j in 0...nbJobs][m in 0...nbMachines] <- interval(0, maxStart);

    // Task duration constraints
    for [j in 0...nbJobs][m in 0...nbMachines]
        constraint length(tasks[j][m]) == processingTime[j][m];

    // Precedence constraints between the tasks of a job
    for [j in 0...nbJobs][k in 0...nbMachines-1]
        constraint tasks[j][machineOrder[j][k]] < tasks[j][machineOrder[j][k + 1]];

    // Sequence of tasks on each machine
    jobsOrder[m in 0...nbMachines] <- list(nbJobs);

    for [m in 0...nbMachines] {
        // Each job has a task scheduled on each machine
        constraint count(jobsOrder[m]) == nbJobs;

        // Disjunctive resource constraints between the tasks on a machine
        constraint and(0...nbJobs-1,
                i => tasks[jobsOrder[m][i]][m] < tasks[jobsOrder[m][i + 1]][m]);
    }

    // Minimize the makespan: end of the last task of the last job
    makespan <- max[j in 0...nbJobs] (end(tasks[j][machineOrder[j][nbMachines - 1]]));
    minimize makespan;
}

/* Parameterize the solver */
function param() {
    if (lsTimeLimit == nil) lsTimeLimit = 60;
}

/* Write the solution in a file with the following format:
 *  - for each machine, the job sequence */
function output() {
    if (outFileName != nil) {
        outFile = io.openWrite(outFileName);
        println("Solution written in file ", outFileName);
        for [m in 0...nbMachines]
            outFile.println[j in 0...nbJobs](jobsOrder[m].value[j], " ");
    }
}
Execution (Windows)
set PYTHONPATH=%LS_HOME%\bin\python
python jobshop.py instances\ft10.txt
Execution (Linux)
export PYTHONPATH=/opt/localsolver_12_5/bin/python
python jobshop.py instances/ft10.txt
import localsolver
import sys


# The input files follow the "Taillard" format
def read_instance(filename):
    with open(filename) as f:
        lines = f.readlines()

    first_line = lines[1].split()
    # Number of jobs
    nb_jobs = int(first_line[0])
    # Number of machines
    nb_machines = int(first_line[1])

    # Processing times for each job on each machine (given in the processing order)
    processing_times_in_processing_order = [[int(lines[i].split()[j])
                                             for j in range(nb_machines)]
                                            for i in range(3, 3 + nb_jobs)]

    # Processing order of machines for each job
    machine_order = [[int(lines[i].split()[j]) - 1 for j in range(nb_machines)]
                     for i in range(4 + nb_jobs, 4 + 2 * nb_jobs)]

    # Reorder processing times: processing_time[j][m] is the processing time of the
    # task of job j that is processed on machine m
    processing_time = [[processing_times_in_processing_order[j][machine_order[j].index(m)]
                        for m in range(nb_machines)]
                       for j in range(nb_jobs)]

    # Trivial upper bound for the start times of the tasks
    max_start = sum(sum(processing_time[j]) for j in range(nb_jobs))

    return nb_jobs, nb_machines, processing_time, machine_order, max_start


def main(instance_file, output_file, time_limit):
    nb_jobs, nb_machines, processing_time, machine_order, max_start = read_instance(instance_file)

    with localsolver.LocalSolver() as ls:
        #
        # Declare the optimization model
        #
        model = ls.model

        # Interval decisions: time range of each task
        # tasks[j][m] is the interval of time of the task of job j which is processed
        # on machine m
        tasks = [[model.interval(0, max_start) for m in range(nb_machines)]
                 for j in range(nb_jobs)]

        # Task duration constraints
        for j in range(nb_jobs):
            for m in range(0, nb_machines):
                model.constraint(model.length(tasks[j][m]) == processing_time[j][m])

        # Create a LocalSolver array in order to be able to access it with "at" operators
        task_array = model.array(tasks)

        # Precedence constraints between the tasks of a job
        for j in range(nb_jobs):
            for k in range(nb_machines - 1):
                model.constraint(
                    tasks[j][machine_order[j][k]] < tasks[j][machine_order[j][k + 1]])

        # Sequence of tasks on each machine
        jobs_order = [model.list(nb_jobs) for m in range(nb_machines)]

        for m in range(nb_machines):
            # Each job has a task scheduled on each machine
            sequence = jobs_order[m]
            model.constraint(model.eq(model.count(sequence), nb_jobs))

            # Disjunctive resource constraints between the tasks on a machine
            sequence_lambda = model.lambda_function(
                lambda i: model.lt(model.at(task_array, sequence[i], m),
                                   model.at(task_array, sequence[i + 1], m)))
            model.constraint(model.and_(model.range(0, nb_jobs - 1), sequence_lambda))

        # Minimize the makespan: end of the last task of the last job
        makespan = model.max([model.end(tasks[j][machine_order[j][nb_machines - 1]])
                             for j in range(nb_jobs)])
        model.minimize(makespan)

        model.close()

        # Parameterize the solver
        ls.param.time_limit = time_limit

        ls.solve()

        #
        # Write the solution in a file with the following format:
        # - for each machine, the job sequence
        #
        if output_file != None:
            final_jobs_order = [list(jobs_order[m].value) for m in range(nb_machines)]
            with open(output_file, "w") as f:
                print("Solution written in file ", output_file)
                for m in range(nb_machines):
                    for j in range(nb_jobs):
                        f.write(str(final_jobs_order[m][j]) + " ")
                    f.write("\n")


if __name__ == '__main__':
    if len(sys.argv) < 2:
        print("Usage: python jobshop.py instance_file [output_file] [time_limit]")
        sys.exit(1)

    instance_file = sys.argv[1]
    output_file = sys.argv[2] if len(sys.argv) >= 3 else None
    time_limit = int(sys.argv[3]) if len(sys.argv) >= 4 else 60
    main(instance_file, output_file, time_limit)
Compilation / Execution (Windows)
cl /EHsc jobshop.cpp -I%LS_HOME%\include /link %LS_HOME%\bin\localsolver125.lib
jobshop instances\ft10.txt
Compilation / Execution (Linux)
g++ jobshop.cpp -I/opt/localsolver_12_5/include -llocalsolver125 -lpthread -o jobshop
./jobshop instances/ft10.txt
#include "localsolver.h"
#include <algorithm>
#include <fstream>
#include <iostream>
#include <limits>
#include <numeric>
#include <vector>

using namespace localsolver;
using namespace std;

class Jobshop {
private:
    // Number of jobs
    int nbJobs;
    // Number of machines
    int nbMachines;
    // Processing order of machines for each job
    vector<vector<int>> machineOrder;
    // Processing time on each machine for each job (given in the machine order)
    vector<vector<int>> processingTime;
    // Trivial upper bound for the start times of the tasks
    int maxStart;

    // LocalSolver
    LocalSolver localsolver;
    // Decision variables: time range of each task
    vector<vector<LSExpression>> tasks;
    // Decision variables: sequence of tasks on each machine
    vector<LSExpression> jobsOrder;
    // Objective = minimize the makespan: end of the last task of the last job
    LSExpression makespan;

public:
    Jobshop() : localsolver() {}

    // The input files follow the "Taillard" format
    void readInstance(const string& fileName) {
        ifstream infile;
        infile.exceptions(ifstream::failbit | ifstream::badbit);
        infile.open(fileName.c_str());

        infile.ignore(numeric_limits<streamsize>::max(), '\n');
        infile >> nbJobs;
        infile >> nbMachines;
        infile.ignore(numeric_limits<streamsize>::max(), '\n');

        // Processing times for each job on each machine (given in the processing order)
        infile.ignore(numeric_limits<streamsize>::max(), '\n');
        vector<vector<int>> processingTimesInProcessingOrder = vector<vector<int>>(nbJobs, vector<int>(nbMachines));
        for (int j = 0; j < nbJobs; ++j) {
            for (int m = 0; m < nbMachines; ++m) {
                infile >> processingTimesInProcessingOrder[j][m];
            }
        }

        // Processing order of machines for each job
        infile.ignore(numeric_limits<streamsize>::max(), '\n');
        infile.ignore(numeric_limits<streamsize>::max(), '\n');
        machineOrder.resize(nbJobs);
        for (int j = 0; j < nbJobs; ++j) {
            machineOrder[j].resize(nbMachines);
            for (int m = 0; m < nbMachines; ++m) {
                int x;
                infile >> x;
                machineOrder[j][m] = x - 1;
            }
        }

        // Reorder processing times: processingTime[j][m] is the processing time of the
        // task of job j that is processed on machine m
        for (int j = 0; j < nbJobs; ++j) {
            processingTime.resize(nbJobs);
            for (int m = 0; m < nbMachines; ++m) {
                processingTime[j].resize(nbMachines);
                vector<int>::iterator findM = find(machineOrder[j].begin(), machineOrder[j].end(), m);
                unsigned int k = distance(machineOrder[j].begin(), findM);
                processingTime[j][m] = processingTimesInProcessingOrder[j][k];
            }
        }

        // Trivial upper bound for the start times of the tasks
        maxStart = 0;
        for (int j = 0; j < nbJobs; ++j)
            maxStart += accumulate(processingTime[j].begin(), processingTime[j].end(), 0);

        infile.close();
    }

    void solve(int timeLimit) {
        // Declare the optimization model
        LSModel model = localsolver.getModel();

        // Interval decisions: time range of each task
        // tasks[j][m] is the interval of time of the task of job j which is processed on machine m
        tasks.resize(nbJobs);
        for (unsigned int j = 0; j < nbJobs; ++j) {
            tasks[j].resize(nbMachines);
            for (unsigned int m = 0; m < nbMachines; ++m) {
                tasks[j][m] = model.intervalVar(0, maxStart);

                // Task duration constraints
                model.constraint(model.length(tasks[j][m]) == processingTime[j][m]);
            }
        }

        // Create a LocalSolver array in order to be able to access it with "at" operators
        LSExpression taskArray = model.array();
        for (int j = 0; j < nbJobs; ++j) {
            taskArray.addOperand(model.array(tasks[j].begin(), tasks[j].end()));
        }

        // Precedence constraints between the tasks of a job
        for (int j = 0; j < nbJobs; ++j) {
            for (int k = 0; k < nbMachines - 1; ++k) {
                model.constraint(tasks[j][machineOrder[j][k]] < tasks[j][machineOrder[j][k + 1]]);
            }
        }

        // Sequence of tasks on each machine
        jobsOrder.resize(nbMachines);
        for (int m = 0; m < nbMachines; ++m) {
            jobsOrder[m] = model.listVar(nbJobs);
        }

        for (int m = 0; m < nbMachines; ++m) {
            // Each job has a task scheduled on each machine
            LSExpression sequence = jobsOrder[m];
            model.constraint(model.eq(model.count(sequence), nbJobs));

            // Disjunctive resource constraints between the tasks on a machine
            LSExpression sequenceLambda = model.createLambdaFunction([&](LSExpression i) {
                return model.at(taskArray, sequence[i], m) < model.at(taskArray, sequence[i + 1], m);
            });
            model.constraint(model.and_(model.range(0, nbJobs - 1), sequenceLambda));
        }

        // Minimize the makespan: end of the last task of the last job
        makespan = model.max();
        for (int j = 0; j < nbJobs; ++j) {
            makespan.addOperand(model.end(tasks[j][machineOrder[j][nbMachines - 1]]));
        }
        model.minimize(makespan);

        model.close();

        // Parameterize the solver
        localsolver.getParam().setTimeLimit(timeLimit);

        localsolver.solve();
    }

    /* Write the solution in a file with the following format:
     *  - for each machine, the job sequence */
    void writeSolution(const string& fileName) {
        ofstream outfile;
        outfile.exceptions(ofstream::failbit | ofstream::badbit);
        outfile.open(fileName.c_str());
        cout << "Solution written in file " << fileName << endl;

        for (int m = 0; m < nbMachines; ++m) {
            LSCollection finalJobsOrder = jobsOrder[m].getCollectionValue();
            for (int j = 0; j < nbJobs; ++j) {
                outfile << finalJobsOrder.get(j) << " ";
            }
            outfile << endl;
        }
        outfile.close();
    }
};

int main(int argc, char** argv) {
    if (argc < 2) {
        cout << "Usage: jobshop instanceFile [outputFile] [timeLimit]" << endl;
        exit(1);
    }

    const char* instanceFile = argv[1];
    const char* outputFile = argc > 2 ? argv[2] : NULL;
    const char* strTimeLimit = argc > 3 ? argv[3] : "60";

    Jobshop model;
    try {
        model.readInstance(instanceFile);
        const int timeLimit = atoi(strTimeLimit);
        model.solve(timeLimit);
        if (outputFile != NULL)
            model.writeSolution(outputFile);
        return 0;
    } catch (const exception& e) {
        cerr << "An error occurred: " << e.what() << endl;
        return 1;
    }
}
Compilation / Execution (Windows)
copy %LS_HOME%\bin\localsolvernet.dll .
csc Jobshop.cs /reference:localsolvernet.dll
Jobshop instances\ft10.txt
using System;
using System.IO;
using localsolver;

public class Jobshop : IDisposable
{
    // Number of jobs
    private int nbJobs;

    // Number of machines
    private int nbMachines;

    // Processing order of machines for each job
    private int[,] machineOrder;

    // Processing time on each machine for each job (given in the machine order)
    private long[,] processingTime;

    // Trivial upper bound for the start times of the tasks
    private long maxStart;

    // LocalSolver
    private LocalSolver localsolver;

    // Decision variables: time range of each task
    private LSExpression[,] tasks;

    // Decision variables: sequence of tasks on each machine
    private LSExpression[] jobsOrder;

    // Objective = minimize the makespan: end of the last task of the last job
    private LSExpression makespan;

    public Jobshop()
    {
        localsolver = new LocalSolver();
    }

    // The input files follow the "Taillard" format
    public void ReadInstance(string fileName)
    {
        using (StreamReader input = new StreamReader(fileName))
        {
            input.ReadLine();
            string[] splitted = input.ReadLine().Split(' ');
            nbJobs = int.Parse(splitted[0]);
            nbMachines = int.Parse(splitted[1]);

            // Processing times for each job on each machine (given in the processing order)
            input.ReadLine();
            long[,] processingTimesInProcessingOrder = new long[nbJobs, nbMachines];
            for (int j = 0; j < nbJobs; ++j)
            {
                splitted = input.ReadLine().Trim().Split(' ');
                for (int m = 0; m < nbMachines; ++m)
                    processingTimesInProcessingOrder[j, m] = long.Parse(splitted[m]);
            }

            // Processing order of machines for each job
            input.ReadLine();
            machineOrder = new int[nbJobs, nbMachines];
            for (int j = 0; j < nbJobs; ++j)
            {
                splitted = input.ReadLine().Trim().Split(' ');
                for (int m = 0; m < nbMachines; ++m)
                    machineOrder[j, m] = int.Parse(splitted[m]) - 1;
            }

            // Reorder processing times: processingTime[j, m] is the processing time of the
            // task of job j that is processed on machine m
            processingTime = new long[nbJobs, nbMachines];
            // Trivial upper bound for the start times of the tasks
            maxStart = 0;
            for (int j = 0; j < nbJobs; ++j)
            {
                for (int m = 0; m < nbMachines; ++m)
                {
                    int machineIndex = nbMachines;
                    for (int k = 0; k < nbMachines; ++k)
                    {
                        if (machineOrder[j, k] == m)
                        {
                            machineIndex = k;
                            break;
                        }
                    }
                    processingTime[j, m] = processingTimesInProcessingOrder[j, machineIndex];
                    maxStart += processingTime[j, m];
                }
            }
        }
    }

    public void Dispose()
    {
        localsolver.Dispose();
    }

    public void Solve(int timeLimit)
    {
        // Declare the optimization model
        LSModel model = localsolver.GetModel();

        // Interval decisions: time range of each task
        // tasks[j][m] is the interval of time of the task of job j which is processed on machine m
        tasks = new LSExpression[nbJobs, nbMachines];
        for (int j = 0; j < nbJobs; ++j)
        {
            for (int m = 0; m < nbMachines; ++m)
            {
                tasks[j, m] = model.Interval(0, maxStart);

                // Task duration constraints
                model.Constraint(model.Length(tasks[j, m]) == processingTime[j, m]);
            }
        }

        // Create a LocalSolver array in order to be able to access it with "at" operators
        LSExpression taskArray = model.Array(tasks);

        // Precedence constraints between the tasks of a job
        for (int j = 0; j < nbJobs; ++j)
        {
            for (int k = 0; k < nbMachines - 1; ++k)
            {
                model.Constraint(tasks[j, machineOrder[j, k]] < tasks[j, machineOrder[j, k + 1]]);
            }
        }

        // Sequence of tasks on each machine
        jobsOrder = new LSExpression[nbMachines];
        for (int m = 0; m < nbMachines; ++m)
            jobsOrder[m] = model.List(nbJobs);

        for (int m = 0; m < nbMachines; ++m)
        {
            // Each job has a task scheduled on each machine
            LSExpression sequence = jobsOrder[m];
            model.Constraint(model.Count(sequence) == nbJobs);

            // Disjunctive resource constraints between the tasks on a machine
            LSExpression sequenceLambda = model.LambdaFunction(
                i => taskArray[sequence[i], m] < taskArray[sequence[i + 1], m]
            );
            model.Constraint(model.And(model.Range(0, nbJobs - 1), sequenceLambda));
        }

        // Minimize the makespan: end of the last task of the last job
        makespan = model.Max();
        for (int j = 0; j < nbJobs; ++j)
            makespan.AddOperand(model.End(tasks[j, machineOrder[j, nbMachines - 1]]));
        model.Minimize(makespan);

        model.Close();

        // Parameterize the solver
        localsolver.GetParam().SetTimeLimit(timeLimit);

        localsolver.Solve();
    }

    /* Write the solution in a file with the following format:
     *  - for each machine, the job sequence */
    public void WriteSolution(string fileName)
    {
        using (StreamWriter output = new StreamWriter(fileName))
        {
            Console.WriteLine("Solution written in file " + fileName);
            for (int m = 0; m < nbMachines; ++m)
            {
                LSCollection finalJobsOrder = jobsOrder[m].GetCollectionValue();
                for (int i = 0; i < nbJobs; ++i)
                {
                    int j = (int)finalJobsOrder.Get(i);
                    output.Write(j + " ");
                }
                output.WriteLine();
            }
        }
    }

    public static void Main(string[] args)
    {
        if (args.Length < 1)
        {
            Console.WriteLine("Usage: Jobshop instanceFile [outputFile] [timeLimit]");
            System.Environment.Exit(1);
        }

        string instanceFile = args[0];
        string outputFile = args.Length > 1 ? args[1] : null;
        string strTimeLimit = args.Length > 2 ? args[2] : "60";

        using (Jobshop model = new Jobshop())
        {
            model.ReadInstance(instanceFile);
            model.Solve(int.Parse(strTimeLimit));
            if (outputFile != null)
                model.WriteSolution(outputFile);
        }
    }
}
Compilation / Execution (Windows)
javac Jobshop.java -cp %LS_HOME%\bin\localsolver.jar
java -cp %LS_HOME%\bin\localsolver.jar;. Jobshop instances\ft10.txt
Compilation / Execution (Linux)
javac Jobshop.java -cp /opt/localsolver_12_5/bin/localsolver.jar
java -cp /opt/localsolver_12_5/bin/localsolver.jar:. Jobshop instances/ft10.txt
import java.util.*;
import java.io.*;
import localsolver.*;

public class Jobshop {
    // Number of jobs
    private int nbJobs;
    // Number of machines
    private int nbMachines;
    // Processing time on each machine for each job (given in the machine order)
    private long[][] processingTime;
    // Processing order of machines for each job
    private int[][] machineOrder;
    // Trivial upper bound for the start times of the tasks
    private long maxStart;

    // LocalSolver
    final LocalSolver localsolver;
    // Decision variables: time range of each task
    private LSExpression[][] tasks;
    // Decision variables: sequence of tasks on each machine
    private LSExpression[] jobsOrder;
    // Objective = minimize the makespan: end of the last task of the last job
    private LSExpression makespan;

    public Jobshop(LocalSolver localsolver) throws IOException {
        this.localsolver = localsolver;
    }

    // The input files follow the "Taillard" format
    public void readInstance(String fileName) throws IOException {
        try (Scanner input = new Scanner(new File(fileName))) {
            input.nextLine();
            nbJobs = input.nextInt();
            nbMachines = input.nextInt();

            input.nextLine();
            input.nextLine();
            // Processing times for each job on each machine (given in the processing order)
            long[][] processingTimesInProcessingOrder = new long[nbJobs][nbMachines];
            for (int j = 0; j < nbJobs; ++j) {
                for (int m = 0; m < nbMachines; ++m) {
                    processingTimesInProcessingOrder[j][m] = input.nextInt();
                }
            }
            // Processing order of machines for each job
            input.nextLine();
            input.nextLine();
            machineOrder = new int[nbJobs][nbMachines];
            for (int j = 0; j < nbJobs; ++j) {
                for (int m = 0; m < nbMachines; ++m) {
                    machineOrder[j][m] = input.nextInt() - 1;
                }
            }
            // Reorder processing times: processingTime[j][m] is the processing time of the
            // task of job j that is processed on machine m
            processingTime = new long[nbJobs][nbMachines];
            // Trivial upper bound for the start times of the tasks
            maxStart = 0;
            for (int j = 0; j < nbJobs; ++j) {
                for (int m = 0; m < nbMachines; ++m) {
                    int machineIndex = nbMachines;
                    for (int k = 0; k < nbMachines; ++k) {
                        if (machineOrder[j][k] == m) {
                            machineIndex = k;
                            break;
                        }
                    }
                    processingTime[j][m] = processingTimesInProcessingOrder[j][machineIndex];
                    maxStart += processingTime[j][m];
                }
            }
        }
    }

    public void solve(int timeLimit) {
        // Declare the optimization model
        LSModel model = localsolver.getModel();

        // Interval decisions: time range of each task
        // tasks[j][m] is the interval of time of the task of job j which is processed
        // on machine m
        tasks = new LSExpression[nbJobs][nbMachines];
        for (int j = 0; j < nbJobs; ++j) {
            for (int m = 0; m < nbMachines; ++m) {
                tasks[j][m] = model.intervalVar(0, maxStart);

                // Task duration constraints
                model.constraint(model.eq(model.length(tasks[j][m]), processingTime[j][m]));
            }
        }

        // Create a LocalSolver array in order to be able to access it with "at"
        // operators
        LSExpression taskArray = model.array(tasks);

        // Precedence constraints between the tasks of a job
        for (int j = 0; j < nbJobs; ++j) {
            for (int k = 0; k < nbMachines - 1; ++k) {
                model.constraint(model.lt(tasks[j][machineOrder[j][k]], tasks[j][machineOrder[j][k + 1]]));
            }
        }

        // Sequence of tasks on each machine
        jobsOrder = new LSExpression[nbMachines];
        for (int m = 0; m < nbMachines; ++m) {
            jobsOrder[m] = model.listVar(nbJobs);
        }

        for (int m = 0; m < nbMachines; ++m) {
            // Each job has a task scheduled on each machine
            LSExpression sequence = jobsOrder[m];
            model.constraint(model.eq(model.count(sequence), nbJobs));

            // Disjunctive resource constraints between the tasks on a machine
            LSExpression mExpr = model.createConstant(m);
            LSExpression sequenceLambda = model
                    .lambdaFunction(i -> model.lt(model.at(taskArray, model.at(sequence, i), mExpr),
                            model.at(taskArray, model.at(sequence, model.sum(i, 1)), mExpr)));
            model.constraint(model.and(model.range(0, nbJobs - 1), sequenceLambda));
        }

        // Minimize the makespan: end of the last task of the last job
        makespan = model.max();
        for (int j = 0; j < nbJobs; ++j) {
            makespan.addOperand(model.end(tasks[j][machineOrder[j][nbMachines - 1]]));
        }
        model.minimize(makespan);

        model.close();

        // Parameterize the solver
        localsolver.getParam().setTimeLimit(timeLimit);

        localsolver.solve();
    }

    /*
     * Write the solution in a file with the following format:
     * - for each machine, the job sequence
     */
    public void writeSolution(String fileName) throws IOException {
        try (PrintWriter output = new PrintWriter(fileName)) {
            System.out.println("Solution written in file " + fileName);

            for (int m = 0; m < nbMachines; ++m) {
                LSCollection finalJobsOrder = jobsOrder[m].getCollectionValue();
                for (int i = 0; i < nbJobs; ++i) {
                    int j = Math.toIntExact(finalJobsOrder.get(i));
                    output.write(j + " ");
                }
                output.write("\n");
            }
        }
    }

    public static void main(String[] args) {
        if (args.length < 1) {
            System.out.println("Usage: java Jobshop instanceFile [outputFile] [timeLimit]");
            System.exit(1);
        }

        String instanceFile = args[0];
        String outputFile = args.length > 1 ? args[1] : null;
        String strTimeLimit = args.length > 2 ? args[2] : "60";

        try (LocalSolver localsolver = new LocalSolver()) {
            Jobshop model = new Jobshop(localsolver);
            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);
        }
    }
}