Preemptive Resource Constrained Project Scheduling Problem¶
Principles learned¶
Set precedence constraints
Relax preemption constraints
Use interval decision variables
Use a lambda expression to compute a sum with a variable number of terms
Problem¶
In the preemptive resource constrained project scheduling problem, a project consists of a set of tasks that have to be scheduled. Each task has a given duration and can be interrupted. There are precedence constraints between the tasks: each task must end before any of its successors can start. There are a set of renewable resources. Each task has a given resource requirement, or weight, (possibly zero) on each resource, representing the amount of resource it consumes while it is being processed. Each resource has a given maximum capacity: it can process several tasks at once, but the sum of the processed tasks’s weights can never exceed this maximum capacity.
The goal is to find a schedule that minimizes the makespan: the time when all tasks have been processed.
Download the exampleData¶
The instances provided follow the Patterson format. The format of the data files is as follows:
First line:
Number of tasks (including two additional dummy tasks with duration 0, the source and the sink)
Number of renewable resources
Second line: Maximum capacity for each resource
From the third line, for each task:
Duration of the task
Resource requirements (weights) for each resource
Number of successors
Task ID for each successor
Program¶
We consider a pseudo-preemptive mode of the Resource-Constrained Project Scheduling Problem (RCPSP), in which each task can be split into at most N subtasks, with N a constant number. For each task, we use N interval decision variables to model the time range of each of its subtasks.
For each task, the sum of the lengths of its subtasks’ intervals is constrained to be equal to the task’s total duration.
The N preemption intervals of each task are ordered.
The precedence constraints are easily written: the last subtask of each task must be placed before the first subtask of any of its successors.
The cumulative resource constraints can be formulated as follows: for each resource, and for each time slot t, the amount of resource consumed by the tasks that are being processed must not exceed the resource’s capacity.
To model these constraints, we sum up, for each resource, the weights of all the tasks placed over a given time slot t.
The makespan to minimize is the time when all the tasks have been processed.
- Execution:
- hexaly prcpsp.hxm inFileName=instances/Pat1.rcp [outFileName=] [hxTimeLimit=]
use io;
// The input files follow The "Patterson" Format
function input() {
local usage = "Usage: hexaly prcpsp.hxm inFileName=instanceFile "
+ "[outFileName=outputFile] [hxTimeLimit=timeLimit]";
if (inFileName == nil) throw usage;
inFile = io.openRead(inFileName);
nbTasks = inFile.readInt();
nbResources = inFile.readInt();
// Maximum capacity of each resource
for [r in 0...nbResources] {
capacity[r] = inFile.readInt();
}
for [i in 0...nbTasks] {
// Duration of each task
duration[i] = inFile.readInt();
for[r in 0...nbResources] {
// Resource weight of resource r required for task i
weight[i][r] = inFile.readInt();
}
nbSuccessors[i] = inFile.readInt();
// Successors of each task i
for [s in 0...nbSuccessors[i]] {
successors[i][s] = inFile.readInt() - 1;
}
}
// Trivial upper bound for the start times of the tasks
horizon = sum[i in 0...nbTasks](duration[i]);
// Number of intervals authorized for each task (pseudo preemption)
maxNbPreemptions = 4;
inFile.close();
}
function model() {
// Interval decisions: time range of each task
// Each task can be split into maxNbPreemptions subtasks
tasks[0...nbTasks][0...maxNbPreemptions] <- interval(0, horizon);
for [i in 0...nbTasks]{
// Task duration constraints
constraint sum[t in 0...maxNbPreemptions](length(tasks[i][t])) == duration[i];
// Precedence constraints between each task's subtasks
for[t in 0...maxNbPreemptions - 1]{
constraint tasks[i][t] < tasks[i][t+1];
}
}
// Precedence constraints between the tasks
for[i in 0...nbTasks][s in 0...nbSuccessors[i]] {
constraint tasks[i][maxNbPreemptions-1] < tasks[successors[i][s]][0];
}
// Makespan: end of the last task
makespan <- max[i in 0...nbTasks] (end(tasks[i][maxNbPreemptions-1]));
// Cumulative resource constraints
for [r in 0...nbResources] {
constraint and(0...makespan,
t => sum[i in 0...nbTasks][k in 0...maxNbPreemptions]
(weight[i][r] * contains(tasks[i][k], t)) <= capacity[r]);
}
// Minimize the makespan
minimize makespan;
}
// Parameterize the solver
function param() {
if (hxTimeLimit == nil) hxTimeLimit = 60;
}
/* Write the solution in a file with the following format:
* - total makespan
* - for each task, the task id, the start and end times of each subtask */
function output() {
if (outFileName != nil) {
outFile = io.openWrite(outFileName);
println("Solution written in file ", outFileName);
outFile.println(makespan.value);
for [i in 0...nbTasks] {
outFile.print(i + 1);
startTime = tasks[i][0].value.start ;
for [k in 0...maxNbPreemptions]{
if (tasks[i][k].value.end != startTime){
outFile.print(" (", startTime, " ", tasks[i][k].value.end,")");
}
if (k < maxNbPreemptions - 1){
startTime = tasks[i][k + 1].value.start;
}
}
outFile.println();
}
}
}
- Execution (Windows)
- set PYTHONPATH=%HX_HOME%\bin\pythonpython prcpsp.py instances\Pat1.rcp
- Execution (Linux)
- export PYTHONPATH=/opt/hexaly_13_0/bin/pythonpython prcpsp.py instances/Pat1.rcp
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])
# Maximum capacity of each resource
capacity = [int(lines[1].split()[r]) for r in range(nb_resources)]
# 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)]
# 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)]
nb_successors[i] = int(line[nb_resources + 1])
successors[i] = [int(line[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))
# Number of intervals authorized for each task (pseudo preemption)
max_nb_preemptions = 4
return (nb_tasks, nb_resources, capacity, duration, weight, nb_successors, successors, horizon, max_nb_preemptions)
def main(instance_file, output_file, time_limit):
nb_tasks, nb_resources, capacity, duration, weight, nb_successors, successors, horizon, max_nb_preemptions = read_instance(
instance_file)
with hexaly.optimizer.HexalyOptimizer() as optimizer:
#
# Declare the optimization model
#
model = optimizer.model
# Interval decisions: time range of each task
# Each task can be split into max_nb_preemtptions subtasks
tasks = [[model.interval(0, horizon) for t in range(max_nb_preemptions)]
for i in range(nb_tasks)]
for i in range(nb_tasks):
# Task duration constraints
model.constraint(
model.sum(model.length(tasks[i][t]) for t in range(max_nb_preemptions)) == duration[i])
# Precedence constraints between each task's subtasks
for t in range(max_nb_preemptions - 1):
model.constraint(tasks[i][t] < tasks[i][t + 1])
# Precedence constraints between the tasks
for i in range(nb_tasks):
for s in range(nb_successors[i]):
model.constraint(tasks[i][max_nb_preemptions - 1] < tasks[successors[i][s]][0])
# Makespan: end of the last task
makespan = model.max([model.end(tasks[i][max_nb_preemptions - 1]) 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][k], t)
for i in range(nb_tasks) for k in range(max_nb_preemptions))
<= capacity[r])
model.constraint(model.and_(model.range(makespan), capacity_respected))
# 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 of each subtask
#
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):
start_time = tasks[i][0].value.start()
f.write(str(i + 1))
for k in range(max_nb_preemptions):
if tasks[i][k].value.end() != start_time :
f.write(" (" + str(start_time) + " " + str(tasks[i][k].value.end()) + ")")
if k < max_nb_preemptions - 1 :
start_time = tasks[i][k + 1].value.start()
f.write("\n")
if __name__ == '__main__':
if len(sys.argv) < 2:
print("Usage: python rcpsp.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 prcpsp.cpp -I%HX_HOME%\include /link %HX_HOME%\bin\hexaly130.libprcpsp instances\Pat1.rcp
- Compilation / Execution (Linux)
- g++ prcpsp.cpp -I/opt/hexaly_13_0/include -lhexaly130 -lpthread -o prcpsp./prcpsp instances/Pat1.rcp
#include "optimizer/hexalyoptimizer.h"
#include <algorithm>
#include <fstream>
#include <iostream>
#include <limits>
#include <numeric>
#include <vector>
using namespace hexaly;
class Prcpsp {
private:
// Number of tasks
int nbTasks;
// Number of resources
int nbResources;
// Maximum capacity of each resource
std::vector<int> capacity;
// Duration of each task
std::vector<int> duration;
// Resource weight of resource r required for task i
std::vector<std::vector<int>> weight;
// 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;
// Number of intervals authorized for each task (pseudo preemption)
int maxNbPreemptions = 4;
// Hexaly Optimizer
HexalyOptimizer optimizer;
// Decision variables: time range of each task
std::vector<std::vector<HxExpression>> tasks;
// Objective = minimize the makespan: end of the last task of the last job
HxExpression makespan;
public:
Prcpsp(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;
// The maximum capacity of each resource
capacity.resize(nbResources);
for (int r = 0; r < nbResources; ++r) {
infile >> capacity[r];
}
duration.resize(nbTasks);
weight.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];
}
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
// Each task can be split into maxNbPreemptions subtasks
tasks.resize(nbTasks);
for (int i = 0; i < nbTasks; ++i) {
tasks[i].resize(maxNbPreemptions);
for (int t = 0; t < maxNbPreemptions; t++){
tasks[i][t] = model.intervalVar(0, horizon);
}
// Task duration constraints
HxExpression taskDuration = model.sum();
for (int t = 0; t < maxNbPreemptions; t++) {
taskDuration.addOperand(model.length(tasks[i][t]));
}
model.constraint(taskDuration == duration[i]);
// Precedence constraints between each task's subtasks
for (int t = 0; t < maxNbPreemptions - 1; t++){
model.constraint(tasks[i][t] < tasks[i][t + 1]);
}
}
// Precedence constraints between the tasks
for (int i = 0; i < nbTasks; ++i) {
for (int s = 0; s < nbSuccessors[i]; ++s) {
model.constraint(tasks[i][maxNbPreemptions - 1] < tasks[successors[i][s]][0]);
}
}
// Makespan: end of the last task
makespan = model.max();
for (int i = 0; i < nbTasks; ++i) {
makespan.addOperand(model.end(tasks[i][maxNbPreemptions - 1]));
}
// 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) {
for (int k = 0; k < maxNbPreemptions; k++){
HxExpression taskWeight = weight[i][r] * model.contains(tasks[i][k], t);
totalWeight.addOperand(taskWeight);
}
}
return model.leq(totalWeight, capacity[r]);
});
model.constraint(model.and_(model.range(0, makespan), capacityRespected));
}
// 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 of each subtask */
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) {
hxint startTime = (tasks[i][0]).getIntervalValue().getStart();
outfile << i + 1;
for (int k = 0; k < maxNbPreemptions; k++){
if ((tasks[i][k]).getIntervalValue().getEnd() != startTime){
outfile << " (" << startTime << " "
<< (tasks[i][k]).getIntervalValue().getEnd() << ")";
if (k < maxNbPreemptions - 1){
startTime = (tasks[i][k + 1]).getIntervalValue().getStart();
}
}
}
outfile << std::endl;
}
outfile.close();
}
};
int main(int argc, char** argv) {
if (argc < 2) {
std::cout << "Usage: Prcpsp 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";
Prcpsp 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;
}
}
- Compilation / Execution (Windows)
- copy %HX_HOME%\bin\Hexaly.NET.dll .csc Prcpsp.cs /reference:Hexaly.NET.dllPrcpsp instances\Pat1.rcp
using System;
using System.IO;
using Hexaly.Optimizer;
public class Prcpsp : IDisposable
{
// Number of tasks
private int nbTasks;
// Number of resources
private int nbResources;
// Maximum capacity of each resource
private int[] capacity;
// Duration of each task
private int[] duration;
// Resource weight of resource r required for task i
private int[,] weight;
// 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;
// Number of intervals authorized for each task (pseudo preemption)
private int maxNbPreemptions = 4;
// 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 Prcpsp(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]);
// The maximum capacity of each resource
capacity = new int[nbResources];
splitted = SplitInput(input);
for (int r = 0; r < nbResources; ++r)
capacity[r] = int.Parse(splitted[r]);
duration = new int[nbTasks];
weight = new int[nbTasks, nbResources];
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]);
nbSuccessors[i] = int.Parse(splitted[nbResources + 1]);
successors[i] = new int[nbSuccessors[i]];
for (int s = 0; s < nbSuccessors[i]; ++s)
successors[i][s] = int.Parse(splitted[s + 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
// Each task can be split into maxNbPreemptions subtasks
tasks = new HxExpression[nbTasks, maxNbPreemptions];
for (int i = 0; i < nbTasks; ++i)
{
for (int t = 0; t < maxNbPreemptions; ++t){
tasks[i, t] = model.Interval(0, horizon);
}
// Task duration constraints
HxExpression taskDuration = model.Sum();
for (int t = 0; t < maxNbPreemptions; ++t){
taskDuration.AddOperand(model.Length(tasks[i, t]));
}
model.Constraint(taskDuration == duration[i]);
// Precedence constraints between each task's subtasks
for (int t = 0; t < maxNbPreemptions - 1; ++t){
model.Constraint(tasks[i, t] < tasks[i, t + 1]);
}
}
// Precedence constraints between the tasks
for (int i = 0; i < nbTasks; ++i)
{
for (int s = 0; s < nbSuccessors[i]; ++s)
{
model.Constraint(tasks[i, maxNbPreemptions - 1] < tasks[successors[i][s], 0]);
}
}
// Makespan: end of the last task
makespan = model.Max();
for (int i = 0; i < nbTasks; ++i)
makespan.AddOperand(model.End(tasks[i, maxNbPreemptions - 1]));
// 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)
{
for (int k = 0; k < maxNbPreemptions; ++k){
totalWeight.AddOperand(weight[i, r] * model.Contains(tasks[i, k], t));
}
}
return totalWeight <= capacity[r];
}
);
model.Constraint(model.And(model.Range(0, makespan), capacityRespected));
}
// 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 of each subtask */
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)
{
long startTime = tasks[i, 0].GetIntervalValue().Start();
output.Write((i + 1));
for (int k = 0; k < maxNbPreemptions; ++k){
if (tasks[i, k].GetIntervalValue().End() != startTime){
output.Write(" (" + startTime + " " + tasks[i, k].GetIntervalValue().End() + ")");
if (k < maxNbPreemptions - 1){
startTime = tasks[i, k + 1].GetIntervalValue().Start();
}
}
}
output.WriteLine();
}
}
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("Usage: Prcpsp 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 (Prcpsp model = new Prcpsp(instanceFile))
{
model.ReadInstance(instanceFile);
model.Solve(int.Parse(strTimeLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
}
- Compilation / Execution (Windows)
- javac Prcpsp.java -cp %HX_HOME%\bin\hexaly.jarjava -cp %HX_HOME%\bin\hexaly.jar;. Prcpsp instances\Pat1.rcp
- Compilation / Execution (Linux)
- javac Prcpsp.java -cp /opt/hexaly_13_0/bin/hexaly.jarjava -cp /opt/hexaly_13_0/bin/hexaly.jar:. Prcpsp instances/Pat1.rcp
import java.util.*;
import java.io.*;
import com.hexaly.optimizer.*;
public class Prcpsp {
// Number of tasks
private int nbTasks;
// Number of resources
private int nbResources;
// Maximum capacity of each resource
private int[] capacity;
// Duration of each task
private int[] duration;
// Resource weight of resource r required for task i
private int[][] weight;
// 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;
// Number of intervals authorized for each task (pseudo preemption)
private int maxNbPreemptions = 4;
// 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 Prcpsp(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();
// The maximum capacity of each resource
capacity = new int[nbResources];
for (int r = 0; r < nbResources; ++r) {
capacity[r] = input.nextInt();
}
duration = new int[nbTasks];
weight = new int[nbTasks][nbResources];
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();
}
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
// Each task can be split into maxNbPreemptions subtasks
tasks = new HxExpression[nbTasks][maxNbPreemptions];
for (int i = 0; i < nbTasks; ++i){
for (int t = 0; t < maxNbPreemptions; ++t) {
tasks[i][t] = model.intervalVar(0, horizon);
}
// Task duration constraints
HxExpression taskDuration = model.sum();
for (int t = 0; t < maxNbPreemptions; ++t) {
taskDuration.addOperand(model.length(tasks[i][t]));
}
model.constraint(model.eq(taskDuration, duration[i]));
// Precedence constraints between each task's subtasks
for (int t = 0; t < maxNbPreemptions - 1; ++t){
model.constraint(model.lt(tasks[i][t], tasks[i][t + 1]));
}
}
// 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][maxNbPreemptions - 1], tasks[successors[i][s]][0]));
}
}
// Makespan: end of the last task
makespan = model.max();
for (int i = 0; i < nbTasks; ++i) {
makespan.addOperand(model.end(tasks[i][maxNbPreemptions - 1]));
}
// 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) {
for (int k = 0; k < maxNbPreemptions; ++k){
totalWeight.addOperand(model.prod(
weight[i][rL],
model.contains(tasks[i][k], t)));
}
}
return model.leq(totalWeight, capacity[rL]);
});
model.constraint(model.and(model.range(0, makespan), capacityRespected));
}
// 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 of each subtask
*/
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) {
long start_time = tasks[i][0].getIntervalValue().getStart();
output.print(i + 1);
for (int k = 0; k < maxNbPreemptions; ++k){
if (tasks[i][k].getIntervalValue().getEnd() != start_time){
output.print(" (" + start_time + " "
+ tasks[i][k].getIntervalValue().getEnd() + ")");
if (k < maxNbPreemptions - 1){
start_time = tasks[i][k + 1].getIntervalValue().getStart();
}
}
}
output.println();
}
}
}
public static void main(String[] args) {
if (args.length < 1) {
System.out.println("Usage: java Prcpsp 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()) {
Prcpsp model = new Prcpsp(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);
}
}
}