Project Scheduling with Production and Consumption of Resources

import hexaly.optimizer
import sys


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

    first_line = lines[0].split()

    # Number of tasks
    nb_tasks = int(first_line[0])

    # Number of resources
    nb_resources = int(first_line[1])

    # Number of inventories
    nb_inventories = int(first_line[2])
    
    second_line = lines[1].split()

    # Maximum capacity of each resource
    capacity = [int(second_line[r]) for r in range(nb_resources)]

    # Initial level of each inventory
    init_level = [int(second_line[r + nb_resources]) for r in range(nb_inventories)]

    # Duration of each task
    duration = [0 for i in range(nb_tasks)]

    # Resource weight of resource r required for task i
    weight = [[] for i in range(nb_tasks)]

    # Inventory consumed at beginning of task i
    start_cons = [[] for i in range(nb_tasks)]

    # Inventory produced at end of task i
    end_prod = [[] for i in range(nb_tasks)]

    # Number of successors
    nb_successors = [0 for i in range(nb_tasks)]

    # Successors of each task i
    successors = [[] for i in range(nb_tasks)]

    for i in range(nb_tasks):
        line = lines[i + 2].split()
        duration[i] = int(line[0])
        weight[i] = [int(line[r + 1]) for r in range(nb_resources)]
        start_cons[i] = [int(line[2*r + nb_resources + 1]) for r in range(nb_inventories)]
        end_prod[i] = [int(line[2*r + nb_resources + 2]) for r in range(nb_inventories)]
        nb_successors[i] = int(line[2*nb_inventories + nb_resources + 1])
        successors[i] = [int(line[2*nb_inventories + nb_resources + 2 + s]) - 1 for s in range(nb_successors[i])]

    # Trivial upper bound for the start times of the tasks
    horizon = sum(duration[i] for i in range(nb_tasks))

    return (nb_tasks, nb_resources, nb_inventories, capacity, init_level, duration, weight, start_cons, end_prod, nb_successors, successors, horizon)


def main(instance_file, output_file, time_limit):
    nb_tasks, nb_resources, nb_inventories, capacity, init_level, duration, weight, start_cons, end_prod, nb_successors, successors, horizon = read_instance(
        instance_file)

    with hexaly.optimizer.HexalyOptimizer() as optimizer:
        #
        # Declare the optimization model
        #
        model = optimizer.model

        # Interval decisions: time range of each task
        tasks = [model.interval(0, horizon) for _ in range(nb_tasks)]

        # Task duration constraints
        for i in range(nb_tasks):
            model.constraint(model.length(tasks[i]) == duration[i])

        # Precedence constraints between the tasks
        for i in range(nb_tasks):
            for s in range(nb_successors[i]):
                model.constraint(tasks[i] < tasks[successors[i][s]])

        # Makespan: end of the last task
        makespan = model.max([model.end(tasks[i]) for i in range(nb_tasks)])

        # Cumulative resource constraints
        for r in range(nb_resources):
            capacity_respected = model.lambda_function(
                lambda t: model.sum(weight[i][r] * model.contains(tasks[i], t)
                                    for i in range(nb_tasks))
                <= capacity[r])
            model.constraint(model.and_(model.range(makespan), capacity_respected))

        # Non-negative inventory constraints
        for r in range(nb_resources):
            inventory_value = model.lambda_function(
                lambda t: model.sum(end_prod[i][r] * (model.end(tasks[i]) <= t)
                                        - start_cons[i][r] * (model.start(tasks[i]) <= t)
                                    for i in range(nb_tasks)) 
                                    + init_level[r]
                >= 0)
            model.constraint(model.and_(model.range(makespan), inventory_value))

        # Minimize the makespan
        model.minimize(makespan)

        model.close()

        # Parameterize the optimizer
        optimizer.param.time_limit = time_limit

        optimizer.solve()

        #
        # Write the solution in a file with the following format:
        # - total makespan
        # - for each task, the task id, the start and end times
        #
        if output_file != None:
            with open(output_file, "w") as f:
                print("Solution written in file", output_file)
                f.write(str(makespan.value) + "\n")
                for i in range(nb_tasks):
                    f.write(str(i + 1) + " " + str(tasks[i].value.start()) + " " + str(tasks[i].value.end()))
                    f.write("\n")


if __name__ == '__main__':
    if len(sys.argv) < 2:
        print("Usage: python rcpsp_producer_consumer.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)

#include "optimizer/hexalyoptimizer.h"
#include <algorithm>
#include <fstream>
#include <iostream>
#include <limits>
#include <numeric>
#include <vector>

using namespace hexaly;

class RcpspProducerConsumer {
private:
    // Number of tasks
    int nbTasks;
    // Number of resources
    int nbResources;
    // Number of inventories
    int nbInventories;
    // Maximum capacity of each resource
    std::vector<int> capacity;
    // Initial level of each inventory
    std::vector<int> initLevel;
    // Duration of each task
    std::vector<int> duration;
    // Resource weight of resource r required for task i
    std::vector<std::vector<int>> weight;
    // Inventory consumed at beginning of task
    std::vector<std::vector<int>> startCons;
    // Inventory produced at end of task
    std::vector<std::vector<int>> endProd;
    // Number of successors
    std::vector<int> nbSuccessors;
    // Successors for each task i
    std::vector<std::vector<int>> successors;
    // Trivial upper bound for the start times of the tasks
    int horizon = 0;

    // Hexaly Optimizer
    HexalyOptimizer optimizer;
    // Decision variables: time range of each task
    std::vector<HxExpression> tasks;
    // Objective = minimize the makespan: end of the last task of the last job
    HxExpression makespan;

public:
    RcpspProducerConsumer(const std::string& fileName) : optimizer() {}

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

        infile >> nbTasks;
        infile >> nbResources;
        infile >> nbInventories;

        // The maximum capacity of each resource
        capacity.resize(nbResources);
        for (int r = 0; r < nbResources; ++r) {
            infile >> capacity[r];
        }
        // The initial level of each inventory
        initLevel.resize(nbInventories);
        for (int r = 0; r < nbInventories; ++r) {
            infile >> initLevel[r];
        }

        duration.resize(nbTasks);
        weight.resize(nbTasks);
        startCons.resize(nbTasks);
        endProd.resize(nbTasks);
        nbSuccessors.resize(nbTasks);
        successors.resize(nbTasks);

        for (int i = 0; i < nbTasks; ++i) {
            infile >> duration[i];
            weight[i].resize(nbResources);
            for (int r = 0; r < nbResources; ++r) {
                infile >> weight[i][r];
            }
            startCons[i].resize(nbInventories);
            endProd[i].resize(nbInventories);
            for (int r = 0; r < nbInventories; ++r) {
                infile >> startCons[i][r];
                infile >> endProd[i][r];
            }
            infile >> nbSuccessors[i];
            successors[i].resize(nbSuccessors[i]);
            for (int s = 0; s < nbSuccessors[i]; ++s) {
                int x;
                infile >> x;
                successors[i][s] = x - 1;
            }
            horizon += duration[i];
        }

        infile.close();
    }

    void solve(int TimeLimit) {
        // Declare the optimization model
        HxModel model = optimizer.getModel();

        // Interval decisions: time range of each task
        tasks.resize(nbTasks);
        for (int i = 0; i < nbTasks; ++i) {
            tasks[i] = model.intervalVar(0, horizon);

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

        // Precedence constraints between the tasks
        for (int i = 0; i < nbTasks; ++i) {
            for (int s = 0; s < nbSuccessors[i]; ++s) {
                model.constraint(tasks[i] < tasks[successors[i][s]]);
            }
        }

        // Makespan: end of the last task
        makespan = model.max();
        for (int i = 0; i < nbTasks; ++i) {
            makespan.addOperand(model.end(tasks[i]));
        }

        // Cumulative resource constraints
        for (int r = 0; r < nbResources; ++r) {
            HxExpression capacityRespected = model.createLambdaFunction([&](HxExpression t) {
                HxExpression totalWeight = model.sum();
                for (int i = 0; i < nbTasks; ++i) {
                    HxExpression taskWeight = weight[i][r] * model.contains(tasks[i], t);
                    totalWeight.addOperand(taskWeight);
                }
                return model.leq(totalWeight, capacity[r]);
            });
            model.constraint(model.and_(model.range(0, makespan), capacityRespected));
        }

        // Non-negative inventory constraints
        for (int r = 0; r < nbInventories; ++r) {
            HxExpression inventoryValue = model.createLambdaFunction([&](HxExpression t) {
                HxExpression totalValue = model.sum();
                totalValue.addOperand(initLevel[r]);
                for (int i = 0; i < nbTasks; ++i) {
                    HxExpression taskValue = endProd[i][r] * (t >= model.end(tasks[i])) - startCons[i][r] * (t >= model.start(tasks[i]));
                    totalValue.addOperand(taskValue);
                }
                return model.geq(totalValue, 0);
            });
            model.constraint(model.and_(model.range(0, makespan), inventoryValue));
        }

        // Minimize the makespan
        model.minimize(makespan);

        model.close();

        // Parameterize the optimizer
        optimizer.getParam().setTimeLimit(TimeLimit);

        optimizer.solve();
    }

    /* Write the solution in a file with the following format:
     *  - total makespan
     *  - for each task, the task id, the start and end times */
    void writeSolution(const std::string& fileName) {
        std::ofstream outfile(fileName.c_str());
        if (!outfile.is_open()) {
            std::cerr << "File " << fileName << " cannot be opened." << std::endl;
            exit(1);
        }
        std::cout << "Solution written in file " << fileName << std::endl;

        outfile << makespan.getValue() << std::endl;
        for (int i = 0; i < nbTasks; ++i) {
            outfile << i + 1 << " " << tasks[i].getIntervalValue().getStart() << " "
                    << tasks[i].getIntervalValue().getEnd() << std::endl;
        }
        outfile.close();
    }
};

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

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

    RcpspProducerConsumer model(instanceFile);
    try {
        model.readInstance(instanceFile);
        const int timeLimit = atoi(strTimeLimit);
        model.solve(timeLimit);
        if (outputFile != NULL)
            model.writeSolution(outputFile);
        return 0;
    } catch (const std::exception& e) {
        std::cerr << "An error occurred: " << e.what() << std::endl;
        return 1;
    }
}

using System;
using System.IO;
using Hexaly.Optimizer;

public class RcpspProducerConsumer : IDisposable
{
    // Number of tasks
    private int nbTasks;

    // Number of resources
    private int nbResources;

    // Number of inventories
    private int nbInventories;

    // Maximum capacity of each resource
    private int[] capacity;

    // Initial level of each inventory
    private int[] initLevel;

    // Duration of each task
    private int[] duration;

    // Resource weight of resource r required for task i
    private int[,] weight;
    
    // Inventory consumed at beginning of task
    private int[,] startCons;

    // Inventory produced at end of task
    private int[,] endProd;

    // Number of successors
    private int[] nbSuccessors;

    // Successors for each task i
    private int[][] successors;

    // Trivial upper bound for the start times of the tasks
    private int horizon = 0;

    // Hexaly Optimizer
    private HexalyOptimizer optimizer;

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

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

    public RcpspProducerConsumer(string fileName)
    {
        optimizer = new HexalyOptimizer();
    }

    string[] SplitInput(StreamReader input)
    {
        string line = input.ReadLine();
        if (line == null)
            return new string[0];
        return line.Split(new[] { '	' }, StringSplitOptions.RemoveEmptyEntries);
    }

    // The input files follow the "Patterson" format
    private void ReadInstance(string fileName)
    {
        using (StreamReader input = new StreamReader(fileName))
        {
            string[] splitted = SplitInput(input);
            if (splitted.Length == 0)
                splitted = SplitInput(input);
            nbTasks = int.Parse(splitted[0]);
            nbResources = int.Parse(splitted[1]);
            nbInventories = int.Parse(splitted[2]);

            // The maximum capacity of each resource
            splitted = SplitInput(input);
            capacity = new int[nbResources];
            for (int r = 0; r < nbResources; ++r)
                capacity[r] = int.Parse(splitted[r]);
            initLevel = new int[nbInventories];
            for (int r = 0; r < nbInventories; ++r)
                initLevel[r] = int.Parse(splitted[r + nbResources]);


            duration = new int[nbTasks];
            weight = new int[nbTasks, nbResources];
            startCons = new int[nbTasks, nbInventories];
            endProd = new int[nbTasks, nbInventories];
            nbSuccessors = new int[nbTasks];
            successors = new int[nbTasks][];

            for (int i = 0; i < nbTasks; ++i)
            {
                splitted = SplitInput(input);
                if (splitted.Length == 0)
                    splitted = SplitInput(input);
                duration[i] = int.Parse(splitted[0]);
                for (int r = 0; r < nbResources; ++r)
                    weight[i, r] = int.Parse(splitted[r + 1]);
                for (int r = 0; r < nbInventories; ++r) {
                    startCons[i, r] = int.Parse(splitted[2*r + nbResources + 1]);
                    endProd[i, r] = int.Parse(splitted[2*r + nbResources + 2]);
                }                   
                nbSuccessors[i] = int.Parse(splitted[2*nbInventories + nbResources + 1]);
                successors[i] = new int[nbSuccessors[i]];
                for (int s = 0; s < nbSuccessors[i]; ++s)
                    successors[i][s] = int.Parse(splitted[s + 2*nbInventories + nbResources + 2]) - 1;
                horizon += duration[i];
            }
        }
    }

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

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

        // Interval decisions: time range of each task
        tasks = new HxExpression[nbTasks];
        for (int i = 0; i < nbTasks; ++i)
        {
            tasks[i] = model.Interval(0, horizon);

            // Task duration constraints
            model.Constraint(model.Length(tasks[i]) == duration[i]);
        }

        // Precedence constraints between the tasks
        for (int i = 0; i < nbTasks; ++i)
        {
            for (int s = 0; s < nbSuccessors[i]; ++s)
            {
                model.Constraint(tasks[i] < tasks[successors[i][s]]);
            }
        }

        // Makespan: end of the last task
        makespan = model.Max();
        for (int i = 0; i < nbTasks; ++i)
            makespan.AddOperand(model.End(tasks[i]));

        // Cumulative resource constraints
        for (int r = 0; r < nbResources; ++r)
        {
            HxExpression capacityRespected = model.LambdaFunction(t =>
                {
                    HxExpression totalWeight = model.Sum();
                    for (int i = 0; i < nbTasks; ++i)
                    {
                        totalWeight.AddOperand(weight[i, r] * model.Contains(tasks[i], t));
                    }
                    return totalWeight <= capacity[r];
                }
            );
            model.Constraint(model.And(model.Range(0, makespan), capacityRespected));
        }

        // Non-negative inventories constraints
        for (int r = 0; r < nbInventories; ++r)
        {
            HxExpression inventoryValue = model.LambdaFunction(t =>
                {
                    HxExpression totalValue = model.Sum();
                    totalValue.AddOperand(initLevel[r]);
                    for (int i = 0; i < nbTasks; ++i)
                    {
                        totalValue.AddOperand(endProd[i,r] * (model.End(tasks[i]) <= t) - startCons[i,r] * (model.Start(tasks[i]) <= t));
                    }
                    return totalValue >= 0;
                }
            );
            model.Constraint(model.And(model.Range(0, makespan), inventoryValue));
        }

        // Minimize the makespan
        model.Minimize(makespan);

        model.Close();

        // Parameterize the optimizer
        optimizer.GetParam().SetTimeLimit(timeLimit);

        optimizer.Solve();
    }

    /* Write the solution in a file with the following format:
     *  - total makespan
     *  - for each task, the task id, the start and end times */
    public void WriteSolution(string fileName)
    {
        using (StreamWriter output = new StreamWriter(fileName))
        {
            Console.WriteLine("Solution written in file " + fileName);
            output.WriteLine(makespan.GetValue());
            for (int i = 0; i < nbTasks; ++i)
            {
                output.Write((i + 1) + " " + tasks[i].GetIntervalValue().Start() + " " + tasks[i].GetIntervalValue().End());
                output.WriteLine();
            }
        }
    }

    public static void Main(string[] args)
    {
        if (args.Length < 1)
        {
            Console.WriteLine("Usage: RcpspProducerConsumer 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 (RcpspProducerConsumer model = new RcpspProducerConsumer(instanceFile))
        {
            model.ReadInstance(instanceFile);
            model.Solve(int.Parse(strTimeLimit));
            if (outputFile != null)
                model.WriteSolution(outputFile);
        }
    }
}

import java.util.*;
import java.io.*;
import com.hexaly.optimizer.*;

public class RcpspProducerConsumer {
    // Number of tasks
    private int nbTasks;
    // Number of resources
    private int nbResources;
    // Number of inventories
    private int nbInventories;
    // Maximum capacity of each resource
    private int[] capacity;
    // Initial level of each inventory
    private int[] initLevel;
    // Duration of each task
    private int[] duration;
    // Resource weight of resource r required for task i
    private int[][] weight;
    // Inventory consumed at beginning of task
    private int[][] startCons;
    // Inventory produced at end of task
    private int[][] endProd;
    // Number of successors
    private int[] nbSuccessors;
    // Successors for each task i
    private int[][] successors;
    // Trivial upper bound for the start times of the tasks
    private int horizon = 0;

    // Hexaly Optimizer
    final HexalyOptimizer optimizer;
    // Decision variables: time range of each task
    private HxExpression[] tasks;
    // Objective = minimize the makespan: end of the last task of the last job
    private HxExpression makespan;

    public RcpspProducerConsumer(HexalyOptimizer optimizer, String fileName) throws IOException {
        this.optimizer = optimizer;
    }

    // The input files follow the "Patterson" format
    private void readInstance(String fileName) throws IOException {
        try (Scanner input = new Scanner(new File(fileName))) {
            nbTasks = input.nextInt();
            nbResources = input.nextInt();
            nbInventories = input.nextInt();

            // The maximum capacity of each resource
            capacity = new int[nbResources];
            for (int r = 0; r < nbResources; ++r) {
                capacity[r] = input.nextInt();
            }
            // The initial level of each inventory
            initLevel = new int[nbInventories];
            for (int r = 0; r < nbInventories; ++r) {
                initLevel[r] = input.nextInt();
            }

            duration = new int[nbTasks];
            weight = new int[nbTasks][nbResources];
            startCons = new int[nbTasks][nbInventories];
            endProd = new int[nbTasks][nbInventories];
            nbSuccessors = new int[nbTasks];
            successors = new int[nbTasks][];
            for (int i = 0; i < nbTasks; ++i) {
                duration[i] = input.nextInt();
                for (int r = 0; r < nbResources; ++r) {
                    weight[i][r] = input.nextInt();
                }
                for (int r = 0; r < nbInventories; ++r) {
                    startCons[i][r] = input.nextInt();
                    endProd[i][r] = input.nextInt();
                }
                nbSuccessors[i] = input.nextInt();
                successors[i] = new int[nbSuccessors[i]];
                for (int s = 0; s < nbSuccessors[i]; ++s) {
                    successors[i][s] = input.nextInt() - 1;
                }
                horizon += duration[i];
            }
        }
    }

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

        // Interval decisions: time range of each task
        tasks = new HxExpression[nbTasks];
        for (int i = 0; i < nbTasks; ++i) {
            tasks[i] = model.intervalVar(0, horizon);

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

        // Precedence constraints between the tasks
        for (int i = 0; i < nbTasks; ++i) {
            for (int s = 0; s < nbSuccessors[i]; ++s) {
                model.constraint(model.lt(tasks[i], tasks[successors[i][s]]));
            }
        }

        // Makespan: end of the last task
        makespan = model.max();
        for (int i = 0; i < nbTasks; ++i) {
            makespan.addOperand(model.end(tasks[i]));
        }

        // Cumulative resource constraints
        for (int r = 0; r < nbResources; ++r) {
            final int rL = r;
            HxExpression capacityRespected = model.lambdaFunction(t -> {
                HxExpression totalWeight = model.sum();
                for (int i = 0; i < nbTasks; ++i) {
                    totalWeight.addOperand(model.prod(
                            weight[i][rL],
                            model.contains(tasks[i], t)));
                }
                return model.leq(totalWeight, capacity[rL]);
            });
            model.constraint(model.and(model.range(0, makespan), capacityRespected));
        }

        // Non-negative inventories constraints
        for (int r = 0; r < nbInventories; ++r) {
            final int rL = r;
            HxExpression inventoryValue = model.lambdaFunction(t -> {
                HxExpression totalValue = model.sum();
                totalValue.addOperand(initLevel[rL]);
                for (int i = 0; i < nbTasks; ++i) {
                    totalValue.addOperand(model.sub(model.prod(
                            endProd[i][rL], 
                            model.leq(model.end(tasks[i]), t)),
                        model.prod(startCons[i][rL],
                                model.leq(model.start(tasks[i]), t))));
                }
                return model.geq(totalValue, 0);
            });
            model.constraint(model.and(model.range(0, makespan), inventoryValue));
        }

        // Minimize the makespan
        model.minimize(makespan);

        model.close();

        // Parameterize the optimizer
        optimizer.getParam().setTimeLimit(timeLimit);

        optimizer.solve();
    }

    /*
     * Write the solution in a file with the following format:
     * - total makespan
     * - for each task, the task id, the start and end times
     */
    public void writeSolution(String fileName) throws IOException {
        try (PrintWriter output = new PrintWriter(fileName)) {
            System.out.println("Solution written in file " + fileName);

            output.println(makespan.getValue());

            for (int i = 0; i < nbTasks; ++i) {
                output.println((i + 1) + " " + tasks[i].getIntervalValue().getStart() + " "
                        + tasks[i].getIntervalValue().getEnd());
            }
        }
    }

    public static void main(String[] args) {
        if (args.length < 1) {
            System.out.println("Usage: java RcpspProducerConsumer 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 (HexalyOptimizer optimizer = new HexalyOptimizer()) {
            RcpspProducerConsumer model = new RcpspProducerConsumer(optimizer, instanceFile);
            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);
        }
    }
}