Assembly Line Balancing (SALBP)¶
Principles learned¶
Set succession constraints
Use intermediate variables
Use set variables with operator “find”
Problem¶
We consider a simple assembly line balancing problem (SALBP) as defined by Prof. Dr. Armin Scholl, Friedrich Schiller University Jena. We have a set of tasks that must be gathered in groups called stations. Each task requires a certain processing time. Moreover, some tasks cannot be performed if some other tasks have not been completed before. Finally, the sum of the tasks’ processing times in each station cannot exceed a given limit. Therefore, the goal is to minimize the number of stations such that the cycle time limit constraints and the tasks’ order are satisfied. On the left diagram, the tasks are the letter circles, the order constraints are represented by arrows and the grey areas are the stations.
Download the exampleData¶
The instances provided come from Alena Otto. They are formatted into the following format:
Number of tasks
Cycle time limit
Tasks’ processing times
Precedence relations
Program¶
This Hexaly model defines a sequence of set variables called “station”. Each set represents a station which can either contain some tasks or be empty. To ensure that each task belongs to an unique station, set variables must form a partition.
We state an upper bound for the number of stations equals to the number of tasks according to the naive solution where each task is contained in a different station.
The number of used stations is computed as the number of non-empty sets and should be minimized. The cycle time constraint is written with a variadic sum of processing times over the tasks of each set. The succession constraints verify that for each task, its station’s number is inferior or equal to its successors’ ones thanks to the “find” operator.
- Execution:
- localsolver assembly_line_balancing.lsp inFileName=instances/instance_n20_1.alb [lsTimeLimit=] [solFileName=]
use io;
/* Read instance data */
function input() {
local usage = "Usage: localsolver assembly_line_balancing.lsp "
+ "inFileName=inputFile [lsTimeLimit=timeLimit] [solFileName=solFile]";
if(inFileName == nil) throw usage;
local inFile = io.openRead(inFileName);
inFile.readln();
// Read number of tasks
nbTasks = inFile.readInt();
maxNbStations = nbTasks;
inFile.readln();
// Read the cycle time limit
cycleTime = inFile.readInt();
for [i in 0...5] inFile.readln();
// Read the processing times
for [i in 0...nbTasks]
processingTime[inFile.readInt() - 1] = inFile.readInt();
inFile.readln();
// Read the successors' relations
successors[i in 0...nbTasks] = {};
local line = inFile.readln().split(",");
while(line.count() > 1) {
local predecessor = line[0].toInt() - 1;
local successor = line[1].toInt() - 1;
successors[predecessor].add(successor);
line = inFile.readln().split(",");
}
inFile.close();
}
/* Declare the optimization model */
function model() {
// Decision variables: station[s] is the set of tasks assigned to station s
station[s in 0...maxNbStations] <- set(nbTasks);
constraint partition[s in 0...maxNbStations](station[s]);
// Objective: nbUsedStations is the total number of used stations
nbUsedStations <- sum[s in 0...maxNbStations](count(station[s]) > 0);
// All stations must respect the cycleTime constraint
timeInStation[s in 0...maxNbStations] <- sum(station[s], i => processingTime[i]);
for [s in 0...maxNbStations]
constraint timeInStation[s] <= cycleTime;
// The stations must respect the succession's order of the tasks
taskStation[i in 0...nbTasks] <- find(station, i);
for [i in 0...nbTasks][j in successors[i]]
constraint taskStation[i] <= taskStation[j];
// Minimization of the number of active stations
minimize nbUsedStations;
}
/* Parametrize the solver */
function param() {
if (lsTimeLimit == nil) lsTimeLimit = 20;
}
/* Write the solution in a file following the format:
* - 1st line: value of the objective
* - 2nd line: number of tasks
* - following lines: task's number, station's number */
function output() {
if(solFileName == nil) return;
local solFile = io.openWrite(solFileName);
solFile.println(nbUsedStations.value);
solFile.println(nbTasks);
for [i in 0...nbTasks]
solFile.println(i + 1, ",", taskStation[i].value + 1);
}
- Execution (Windows)
- set PYTHONPATH=%LS_HOME%\bin\pythonpython assembly_line_balancing.py instances\instance_n20_1.alb
- Execution (Linux)
- export PYTHONPATH=/opt/localsolver_12_5/bin/pythonpython assembly_line_balancing.py instances/instance_n20_1.alb
import localsolver
import sys
#
# Functions to read the instances
#
def read_elem(filename):
with open(filename) as f:
return [str(elem) for elem in f.read().split()]
def read_instance(instance_file):
file_it = iter(read_elem(instance_file))
for _ in range(3):
next(file_it)
# Read number of tasks
nb_tasks = int(next(file_it))
max_nb_stations = nb_tasks
for _ in range(2):
next(file_it)
# Read the cycle time limit
cycle_time = int(next(file_it))
for _ in range(5):
next(file_it)
# Read the processing times
processing_time_dict = {}
for _ in range(nb_tasks):
task = int(next(file_it)) - 1
processing_time_dict[task] = int(next(file_it))
for _ in range(2):
next(file_it)
processing_time = [elem[1] for elem in sorted(processing_time_dict.items(),
key=lambda x: x[0])]
# Read the successors' relations
successors = {}
while True:
try:
pred, succ = next(file_it).split(',')
pred = int(pred) - 1
succ = int(succ) - 1
if pred in successors:
successors[pred].append(succ)
else:
successors[pred] = [succ]
except:
break
return nb_tasks, max_nb_stations, cycle_time, processing_time, successors
def main(instance_file, output_file, time_limit):
nb_tasks, max_nb_stations, cycle_time, processing_time_data, \
successors_data = read_instance(instance_file)
with localsolver.LocalSolver() as ls:
#
# Declare the optimization model
#
model = ls.model
# Decision variables: station_vars[s] is the set of tasks assigned to station s
station_vars = [model.set(nb_tasks) for s in range(max_nb_stations)]
stations = model.array(station_vars)
model.constraint(model.partition(stations))
# Objective: nb_used_stations is the total number of used stations
nb_used_stations = model.sum(
(model.count(station_vars[s]) > 0) for s in range(max_nb_stations))
# All stations must respect the cycleTime constraint
processing_time = model.array(processing_time_data)
time_lambda = model.lambda_function(lambda i: processing_time[i])
time_in_station = [model.sum(station_vars[s], time_lambda)
for s in range(max_nb_stations)]
for s in range(max_nb_stations):
model.constraint(time_in_station[s] <= cycle_time)
# The stations must respect the succession's order of the tasks
task_station = [model.find(stations, i) for i in range(nb_tasks)]
for i in range(nb_tasks):
if i in successors_data.keys():
for j in successors_data[i]:
model.constraint(task_station[i] <= task_station[j])
# Minimization of the number of active stations
model.minimize(nb_used_stations)
model.close()
# Parameterize the solver
ls.param.time_limit = time_limit
ls.solve()
# Write the solution in a file following the format:
# - 1st line: value of the objective
# - 2nd line: number of tasks
# - following lines: task's number, station's number
if output_file is not None:
with open(output_file, 'w') as f:
f.write("%d\n" % nb_used_stations.value)
f.write("%d\n" % nb_tasks)
for i in range(nb_tasks):
f.write("{},{}\n".format(i + 1, task_station[i].value + 1))
if __name__ == '__main__':
if len(sys.argv) < 2:
print("Usage: python assembly_line_balancing.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 20
main(instance_file, output_file, time_limit)
- Compilation / Execution (Windows)
- cl /EHsc assembly_line_balancing.cpp -I%LS_HOME%\include /link %LS_HOME%\bin\localsolver125.libassembly_line_balancing instances\instance_n20_1.alb
- Compilation / Execution (Linux)
- g++ assembly_line_balancing.cpp -I/opt/localsolver_12_5/include -llocalsolver125 -lpthread -o assembly_line_balancing./assembly_line_balancing instances/instance_n20_1.alb
#include "localsolver.h"
#include <fstream>
#include <iostream>
#include <vector>
using namespace localsolver;
using namespace std;
class AssemblyLineBalancing {
private:
// Data from the problem
int nbTasks;
int nbMaxStations;
int cycleTime;
string tmp;
vector<int> processingTimeData;
vector<vector<int>> successorsData;
// LocalSolver
LocalSolver localsolver;
// Decision variables
vector<LSExpression> stationVars;
// Intermediate expressions
vector<LSExpression> timeInStation;
vector<LSExpression> taskStation;
// Objective
LSExpression nbUsedStations;
public:
/* Read instance data */
void readInstance(const string& fileName) {
ifstream infile;
infile.exceptions(ifstream::failbit | ifstream::badbit);
infile.open(fileName.c_str());
for (int i = 0; i < 3; ++i)
infile >> tmp;
// Read number of tasks
infile >> nbTasks;
nbMaxStations = nbTasks;
processingTimeData.resize(nbTasks);
successorsData.resize(nbTasks);
for (int i = 0; i < 2; ++i)
infile >> tmp;
// Read the cycle time limit
infile >> cycleTime;
for (int i = 0; i < 5; ++i)
infile >> tmp;
// Read the processing times
for (int i = 0; i < nbTasks; ++i) {
int task;
infile >> task;
infile >> processingTimeData[task - 1];
}
for (int i = 0; i < 2; ++i)
infile >> tmp;
// Read the successors' relations
string delimiter = ",";
while (infile.eof() != true) {
string relation;
infile >> relation;
string predecessor = relation.substr(0, relation.find(delimiter));
string successor = relation.substr(relation.find(delimiter) + 1, relation.size());
if (predecessor == relation)
break;
successorsData[stoi(predecessor) - 1].push_back(stoi(successor) - 1);
}
infile.close();
}
void solve(int limit) {
// Declare the optimization model
LSModel model = localsolver.getModel();
// Decision variables: stationVars[s] is the set of tasks assigned to station s
stationVars.resize(nbMaxStations);
LSExpression stations = model.array();
for (int s = 0; s < nbMaxStations; ++s) {
stationVars[s] = model.setVar(nbTasks);
stations.addOperand(stationVars[s]);
}
model.constraint(model.partition(stations));
// Objective: nbUsedStations is the total number of used stations
nbUsedStations = model.sum();
for (int s = 0; s < nbMaxStations; ++s)
nbUsedStations.addOperand((model.count(stationVars[s]) > 0));
// All stations must respect the cycleTime constraint
timeInStation.resize(nbMaxStations);
LSExpression processingTime = model.array(processingTimeData.begin(), processingTimeData.end());
LSExpression timeLambda = model.lambdaFunction([&](LSExpression i) { return processingTime[i]; });
for (int s = 0; s < nbMaxStations; ++s) {
timeInStation[s] = model.sum(stationVars[s], timeLambda);
model.constraint(timeInStation[s] <= cycleTime);
}
// The stations must respect the succession's order of the tasks
taskStation.resize(nbTasks);
for (int i = 0; i < nbTasks; ++i) {
taskStation[i] = model.find(stations, i);
}
for (int i = 0; i < nbTasks; ++i)
for (int j : successorsData[i])
model.constraint(taskStation[i] <= taskStation[j]);
// Minimization of the number of active stations
model.minimize(nbUsedStations);
model.close();
// Parametrize the solver
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
}
/* Write the solution in a file following the format:
* - 1st line: value of the objective
* - 2nd line: number of tasks
* - following lines: task's number, station's number */
void writeSolution(const string& fileName) {
ofstream outfile;
outfile.exceptions(ofstream::failbit | ofstream::badbit);
outfile.open(fileName.c_str());
outfile << nbUsedStations.getIntValue() << endl;
outfile << nbTasks << endl;
for (int i = 0; i < nbTasks; ++i)
outfile << i + 1 << "," << taskStation[i].getIntValue() + 1 << endl;
}
};
int main(int argc, char** argv) {
if (argc < 2) {
cerr << "Usage: assembly_line_balancing 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] : "20";
try {
AssemblyLineBalancing 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 %LS_HOME%\bin\localsolvernet.dll .csc AssemblyLineBalancing.cs /reference:localsolvernet.dllAssemblyLineBalancing instances\instance_n20_1.alb
using System;
using System.IO;
using System.Collections.Generic;
using localsolver;
public class AssemblyLineBalancing : IDisposable
{
// Data from the problem
public int nbTasks;
public int nbMaxStations;
public int cycleTime;
public int[] processingTimeData;
public List<int>[] successorsData;
// LocalSolver
LocalSolver localsolver;
// Decision variables
LSExpression[] stationVars;
// Intermediate expressions
LSExpression[] timeInStation;
LSExpression[] taskStation;
// Objective
LSExpression nbUsedStations;
public AssemblyLineBalancing()
{
localsolver = new LocalSolver();
}
public void Dispose()
{
if (localsolver != null)
localsolver.Dispose();
}
/* Read instance data */
void ReadInstance(string fileName)
{
using (StreamReader input = new StreamReader(fileName))
{
string[] line;
input.ReadLine();
// Read number of tasks
nbTasks = int.Parse(input.ReadLine());
nbMaxStations = nbTasks;
processingTimeData = new int[nbTasks];
successorsData = new List<int>[nbTasks];
for (int i = 0; i < 2; ++i)
input.ReadLine();
// Read the cycle time limit
cycleTime = int.Parse(input.ReadLine());
for (int i = 0; i < 6; ++i)
input.ReadLine();
// Read the processing times
for (int i = 0; i < nbTasks; ++i)
{
line = input.ReadLine().Split();
processingTimeData[i] = int.Parse(line[1]);
}
for (int i = 0; i < 2; ++i)
input.ReadLine();
// Read the successors' relations
while (true)
{
line = input.ReadLine().Split(',');
if (line[0] == "")
break;
int predecessor = int.Parse(line[0]) - 1;
int successor = int.Parse(line[1]) - 1;
if (successorsData[predecessor] == null)
successorsData[predecessor] = new List<int>();
successorsData[predecessor].Add(successor);
}
}
}
void Solve(int limit)
{
// Declare the optimization model
LSModel model = localsolver.GetModel();
// Decision variables: stationVars[s] is the set of tasks assigned to station s
stationVars = new LSExpression[nbMaxStations];
LSExpression stations = model.Array();
for (int s = 0; s < nbMaxStations; ++s)
{
stationVars[s] = model.Set(nbTasks);
stations.AddOperand(stationVars[s]);
}
model.Constraint(model.Partition(stations));
// Objective: nbUsedStations is the total number of used stations
nbUsedStations = model.Sum();
for (int s = 0; s < nbMaxStations; ++s)
nbUsedStations.AddOperand(model.Count(stationVars[s]) > 0);
// All stations must respect the cycleTime constraint
timeInStation = new LSExpression[nbMaxStations];
LSExpression processingTime = model.Array(processingTimeData);
LSExpression timeLambda = model.LambdaFunction(i => processingTime[i]);
for (int s = 0; s < nbMaxStations; ++s)
{
timeInStation[s] = model.Sum(stationVars[s], timeLambda);
model.Constraint(timeInStation[s] <= cycleTime);
}
// The stations must respect the succession's order of the tasks
taskStation = new LSExpression[nbTasks];
for (int i = 0; i < nbTasks; ++i)
taskStation[i] = model.Find(stations, i);
for (int i = 0; i < nbTasks; ++i)
if (successorsData[i] != null)
foreach (int j in successorsData[i])
model.Constraint(taskStation[i] <= taskStation[j]);
// Minimization of the number of active stations
model.Minimize(nbUsedStations);
model.Close();
// Parametrize the solver
localsolver.GetParam().SetTimeLimit(limit);
localsolver.Solve();
}
/* Write the solution in a file following the format:
* - 1st line: value of the objective
* - 2nd line: number of tasks
* - following lines: task's number, station's number */
void WriteSolution(string fileName)
{
using (StreamWriter output = new StreamWriter(fileName))
{
output.WriteLine(nbUsedStations.GetIntValue());
output.WriteLine(nbTasks);
for (int i = 0; i < nbTasks; ++i)
{
output.Write(i + 1);
output.Write(',');
output.WriteLine(taskStation[i].GetIntValue() + 1);
}
}
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("Usage: AssemblyLineBalancing 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] : "20";
using (AssemblyLineBalancing model = new AssemblyLineBalancing())
{
model.ReadInstance(instanceFile);
model.Solve(int.Parse(strTimeLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
}
- Compilation / Execution (Windows)
- javac AssemblyLineBalancing.java -cp %LS_HOME%\bin\localsolver.jarjava -cp %LS_HOME%\bin\localsolver.jar;. AssemblyLineBalancing instances\instance_n20_1.alb
- Compilation / Execution (Linux)
- javac AssemblyLineBalancing.java -cp /opt/localsolver_12_5/bin/localsolver.jarjava -cp /opt/localsolver_12_5/bin/localsolver.jar:. AssemblyLineBalancing instances/instance_n20_1.alb
import java.util.*;
import java.io.*;
import localsolver.*;
public class AssemblyLineBalancing {
// Data from the problem
int nbTasks;
int nbMaxStations;
int cycleTime;
int[] processingTimeData;
ArrayList<ArrayList<Integer>> successorsData;
// LocalSolver
private final LocalSolver localsolver;
// Decision variables
private LSExpression[] stationVars;
// Intermediate expressions
private LSExpression[] timeInStation;
private LSExpression[] taskStation;
// Objective
private LSExpression nbUsedStations;
private AssemblyLineBalancing(LocalSolver localsolver) {
this.localsolver = localsolver;
}
/* Read instance data */
private void readInstance(String fileName) throws IOException {
try (Scanner input = new Scanner(new File(fileName))) {
input.nextLine();
// Read number of tasks
nbTasks = input.nextInt();
nbMaxStations = nbTasks;
processingTimeData = new int[nbTasks];
successorsData = new ArrayList<ArrayList<Integer>>(nbTasks);
for (int i = 0; i < nbTasks; ++i)
successorsData.add(i, new ArrayList<Integer>());
for (int i = 0; i < 3; ++i)
input.nextLine();
// Read the cycle time limit
cycleTime = input.nextInt();
for (int i = 0; i < 7; ++i)
input.nextLine();
// Read the processing times
for (int i = 0; i < nbTasks; ++i)
processingTimeData[input.nextInt() - 1] = input.nextInt();
for (int i = 0; i < 3; ++i)
input.nextLine();
// Read the successors' relations
String line = input.nextLine();
while (!line.isEmpty()) {
String lineSplit[] = line.split(",");
int predecessor = Integer.parseInt(lineSplit[0]) - 1;
int successor = Integer.parseInt(lineSplit[1]) - 1;
successorsData.get(predecessor).add(successor);
line = input.nextLine();
}
}
}
private void solve(int limit) {
// Declare the optimization model
LSModel model = localsolver.getModel();
// Decision variables: stationVars[s] is the set of tasks assigned to station s
stationVars = new LSExpression[nbMaxStations];
LSExpression stations = model.array();
for (int s = 0; s < nbMaxStations; ++s) {
stationVars[s] = model.setVar(nbTasks);
stations.addOperand(stationVars[s]);
}
model.constraint(model.partition(stations));
// Objective: nbUsedStations is the total number of used stations
nbUsedStations = model.sum();
for (int s = 0; s < nbMaxStations; ++s) {
nbUsedStations.addOperand(model.gt(model.count(stationVars[s]), 0));
}
// All stations must respect the cycleTime constraint
timeInStation = new LSExpression[nbMaxStations];
LSExpression processingTime = model.array(processingTimeData);
LSExpression timeLambda = model.lambdaFunction(i -> model.at(processingTime, i));
for (int s = 0; s < nbMaxStations; ++s) {
timeInStation[s] = model.sum(stationVars[s], timeLambda);
model.constraint(model.leq(timeInStation[s], cycleTime));
}
// The stations must respect the succession's order of the tasks
taskStation = new LSExpression[nbTasks];
for (int i = 0; i < nbTasks; ++i) {
taskStation[i] = model.find(stations, i);
}
for (int i = 0; i < nbTasks; ++i) {
ArrayList<Integer> successors_i = successorsData.get(i);
for (int j : successors_i) {
model.constraint(model.leq(taskStation[i], taskStation[j]));
}
}
// Minimization of the number of active stations
model.minimize(nbUsedStations);
model.close();
// Parametrize the solver
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
}
/*
* Write the solution in a file following the format:
* - 1st line: value of the objective
* - 2nd line: number of tasks
* - following lines: task's number, station's number
*/
void writeSolution(String fileName) throws IOException {
try (PrintWriter output = new PrintWriter(new FileWriter(fileName))) {
output.println(nbUsedStations.getIntValue());
output.println(nbTasks);
for (int i = 0; i < nbTasks; ++i) {
output.print(i + 1);
output.print(",");
output.println(taskStation[i].getIntValue() + 1);
}
}
}
public static void main(String[] args) {
if (args.length < 1) {
System.err.println("Usage: AssemblyLineBalancing 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] : "20";
try (LocalSolver localsolver = new LocalSolver()) {
AssemblyLineBalancing model = new AssemblyLineBalancing(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);
}
}
}