public void test_performing_algorithm(int maxIterations, int populationCount, float evaporationRate, Core.JobScheduling.Base.SchedulingProblem schedulingProblem, int expectedResultTimeSpan)
{
// Arrange
var algorithm = new AntColonyOptimizationAlgorithm(maxIterations, populationCount, evaporationRate, schedulingProblem);
// Act
var result = algorithm.Perform();
// Assert
Assert.AreEqual(expectedResultTimeSpan, result.TimeSpan);
if (schedulingProblem is Core.JobScheduling.OpenShop.SchedulingProblem)
{
return;
}
foreach (var job in schedulingProblem.Jobs)
{
var scheduledOperations = result.Machines
.Select(m => m.Operations.FindLast(o => o.Operation.JobId == job.Id))
.OrderBy(o => o.StartTime)
.ToList();
for (int i = 0; i < job.Operations.Count; i++)
{
Assert.AreEqual(job.Operations[i].MachineId, scheduledOperations[i].Operation.MachineId);
if (i > 0)
{
int previousOperationEndTime = scheduledOperations[i - 1].StartTime + scheduledOperations[i - 1].Operation.ProcessingTime;
Assert.IsTrue(scheduledOperations[i].StartTime >= previousOperationEndTime);
}
}
}
}
public void test_perform_ant_colony_optimization_algorithm(string directory, string fileName, SchedulingProblemType schedulingProblemType, int maxIterations, int populationCount, float evaporationRate)
{
// Arrange
var path = Directory.GetParent(Directory.GetCurrentDirectory()).Parent.Parent.FullName + "/IntegrationTests/TestInstances/" + directory;
var resultsPath = Directory.GetParent(Directory.GetCurrentDirectory()).Parent.Parent.FullName + "/IntegrationTests/TestResults/ACO";
// Act
var inputData = FileHelper.ReadFile(path, fileName);
var schedulingProblem = SchedulingProblemFactory.Create(schedulingProblemType, inputData);
var algorithm = new AntColonyOptimizationAlgorithm(maxIterations, populationCount, evaporationRate, schedulingProblem);
var watch = System.Diagnostics.Stopwatch.StartNew();
var result = algorithm.Perform();
watch.Stop();
// Assert
Assert.IsTrue(result.IsCorrect(schedulingProblem.Jobs));
// Writing result to file
var executionTime = watch.Elapsed.ToString("hh\\:mm\\:ss");
FileHelper.WriteToFile(resultsPath + "/" + directory, fileName,
$@"maxIterations: {maxIterations}, populationCount: {populationCount}, evaporationRate: {evaporationRate}, timeSpan: {result.TimeSpan}, time: {executionTime}"
);
}
public void test_initializing_algorithm(int maxIterations, int populationCount, float evaporationRate, Core.JobScheduling.Base.SchedulingProblem schedulingProblem, int expectedNodesCount, int expectedPathsCount)
{
// Act
var algorithm = new AntColonyOptimizationAlgorithm(maxIterations, populationCount, evaporationRate, schedulingProblem);
// Assert
Assert.AreEqual(populationCount, algorithm.GetPopulation().Count);
Assert.AreEqual(expectedNodesCount, algorithm.GetNodes().Count);
Assert.AreEqual(expectedPathsCount, algorithm.GetPaths().Count);
}
static void Main(string[] args)
{
var runner = new Runner();
var app = new CommandLineApplication();
app.Description = "Nature-inspired algorithms in jobs scheduling problems";
app.HelpOption("-?|-h|--help");
app.Command("get-test-instances", (command) => {
command.Description = "Download Taillard tests instances";
command.HelpOption("-?|-h|--help");
command.OnExecute(() => {
Console.WriteLine("Downloading...");
runner.DownloadTestInstances();
Console.WriteLine("Downloading finished.");
return(0);
});
});
app.Command("co", (command) => {
command.Description = "Solve job scheduling problem using Cockroach Optimization algorithm.";
command.HelpOption("-?|-h|--help");
var populationSizeOption = command.Option("-s|--size", "Specify population size", CommandOptionType.SingleValue);
var iterationsNumberOption = command.Option("-i|--iterations", "Specify number of iterations", CommandOptionType.SingleValue);
var visualOption = command.Option("-v|--visual", "Specify visual parameter", CommandOptionType.SingleValue);
var maxStepOption = command.Option("-ms|--max-step", "Specify max step size", CommandOptionType.SingleValue);
var jobTypeOption = command.Option("-js|--jobs-system", "Specify jobs system: \"f\", \"j\" or \"o\"", CommandOptionType.SingleValue);
var inputFileOption = command.Option("-if|--input-file", "Specify input file", CommandOptionType.SingleValue);
var saveResultOption = command.Option("-sr|--save-result", "Specify if save result", CommandOptionType.NoValue);
var executionsNumberOption = command.Option("-e|--executions", "Specify number of executions", CommandOptionType.SingleValue);
command.OnExecute(() => {
var populationSize = populationSizeOption.HasValue() ? Int16.Parse(populationSizeOption.Value()) : 10;
var iterationNumber = iterationsNumberOption.HasValue() ? Int16.Parse(iterationsNumberOption.Value()) : 20;
var visual = visualOption.HasValue() ? Int16.Parse(visualOption.Value()) : 10;
var maxStep = maxStepOption.HasValue() ? Int16.Parse(maxStepOption.Value()) : 4;
var jobType = jobTypeOption.HasValue() ? jobTypeOption.Value() : "f";
var inputFile = inputFileOption.HasValue() ? inputFileOption.Value() : "TestInstances/flowshop/tai20_5/0.txt";
var saveResult = saveResultOption.HasValue() ? true : false;
var executions = executionsNumberOption.HasValue() ? Int16.Parse(executionsNumberOption.Value()) : 1;
var optionsString =
$" - population size:\t {populationSize}, {Environment.NewLine}"
+ $" - iterations number:\t {iterationNumber}. {Environment.NewLine}"
+ $" - visual parameter:\t {visual}, {Environment.NewLine}"
+ $" - max step paramter:\t {maxStep}, {Environment.NewLine}"
+ $" - job scheduling type:\t {jobType}, {Environment.NewLine}"
+ $" - input file:\t\t {inputFile} {Environment.NewLine}"
+ $" - save results:\t {saveResult} {Environment.NewLine}"
+ $" - executions number:\t {executions} {Environment.NewLine}";
Console.WriteLine($"{Environment.NewLine}"
+ $"Starting perform CO algorithm with given options: {Environment.NewLine}"
+ optionsString
);
var inputData = FileHelper.ReadFile(_inputDataDirectory, inputFile);
var schedulingProblem = SchedulingProblemFactory.Create(jobType, inputData);
var algorithm = new CockroachAlgorithm(maxStep, visual, iterationNumber, populationSize, schedulingProblem);
var stringResult = optionsString.Replace("\t", "") + Environment.NewLine;
for (int i = 0; i < executions; i++)
{
if (executions > 0)
{
Console.WriteLine("Execution number: " + (i + 1));
}
var result = algorithm.Perform();
stringResult += Environment.NewLine + result.TimeSpan;
Console.WriteLine(Environment.NewLine + "Result: " + result.TimeSpan);
}
if (saveResult)
{
DateTime time = DateTime.Now;
string format = "dd.MM.yyyy_HH:mm:ss";
var fileName = "CO_" + time.ToString(format) + ".txt";
Console.WriteLine("Saving results to " + fileName);
FileHelper.CreateFile(_resultsDirectory, fileName, stringResult);
}
Console.WriteLine(Environment.NewLine + "Calculations finished.");
return(0);
});
});
app.Command("aco", (command) => {
command.Description = "Solve job scheduling problem using Ant Colony Optimization algorithm.";
command.HelpOption("-?|-h|--help");
var populationSizeOption = command.Option("-s|--size", "Specify population size", CommandOptionType.SingleValue);
var iterationsNumberOption = command.Option("-i|--iterations", "Specify number of iterations", CommandOptionType.SingleValue);
var evaporationRateOption = command.Option("-p|--evaporation", "Specify value of evaporation rate parameter", CommandOptionType.SingleValue);
var laidPheromoneOption = command.Option("-q", "Specify value of laid pheromone parameter Q", CommandOptionType.SingleValue);
var jobTypeOption = command.Option("-js|--jobs-system", "Specify jobs system: \"f\", \"j\" or \"o\"", CommandOptionType.SingleValue);
var inputFileOption = command.Option("-if|--input-file", "Specify input file", CommandOptionType.SingleValue);
var saveResultOption = command.Option("-sr|--save-result", "Specify if save result", CommandOptionType.NoValue);
var executionsNumberOption = command.Option("-e|--executions", "Specify number of executions", CommandOptionType.SingleValue);
command.OnExecute(() => {
var populationSize = populationSizeOption.HasValue() ? Int16.Parse(populationSizeOption.Value()) : 10;
var iterationNumber = iterationsNumberOption.HasValue() ? Int16.Parse(iterationsNumberOption.Value()) : 5;
var evaporationRate = evaporationRateOption.HasValue() ? float.Parse(evaporationRateOption.Value()) : 0.2F;
var laidPheromone = laidPheromoneOption.HasValue() ? float.Parse(laidPheromoneOption.Value()) : 1F;
var jobType = jobTypeOption.HasValue() ? jobTypeOption.Value() : "f";
var inputFile = inputFileOption.HasValue() ? inputFileOption.Value() : "TestInstances/flowshop/tai20_5/0.txt";
var saveResult = saveResultOption.HasValue() ? true : false;
var executions = executionsNumberOption.HasValue() ? Int16.Parse(executionsNumberOption.Value()) : 1;
var optionsString =
$" - population size:\t {populationSize}, {Environment.NewLine}"
+ $" - iterations number:\t {iterationNumber}. {Environment.NewLine}"
+ $" - evaporation rate:\t {evaporationRate}, {Environment.NewLine}"
+ $" - laid pheromone parameter Q:\t {laidPheromone}, {Environment.NewLine}"
+ $" - job scheduling type:\t {jobType}, {Environment.NewLine}"
+ $" - input file:\t\t {inputFile} {Environment.NewLine}"
+ $" - save results:\t {saveResult} {Environment.NewLine}"
+ $" - executions number:\t {executions} {Environment.NewLine}";
Console.WriteLine($"{Environment.NewLine}"
+ $"Starting perform ACO algorithm with given options: {Environment.NewLine}"
+ optionsString
);
var inputData = FileHelper.ReadFile(_inputDataDirectory, inputFile);
var schedulingProblem = SchedulingProblemFactory.Create(jobType, inputData);
var algorithm = new AntColonyOptimizationAlgorithm(iterationNumber, populationSize, evaporationRate, schedulingProblem, laidPheromone);
var stringResult = optionsString.Replace("\t", "") + Environment.NewLine;
for (int i = 0; i < executions; i++)
{
if (executions > 0)
{
Console.WriteLine("Execution number: " + (i + 1));
}
var result = algorithm.Perform();
stringResult += Environment.NewLine + result.TimeSpan;
Console.WriteLine(Environment.NewLine + "Result: " + result.TimeSpan);
}
if (saveResult)
{
DateTime time = DateTime.Now;
string format = "dd.MM.yyyy_HH:mm:ss";
var fileName = "ACO_" + time.ToString(format) + ".txt";
Console.WriteLine("Saving results to " + fileName);
FileHelper.CreateFile(_resultsDirectory, fileName, stringResult);
}
Console.WriteLine(Environment.NewLine + "Calculations finished.");
return(0);
});
});
app.Command("ga", (command) => {
command.Description = "Solve job scheduling problem using Genetic Algorithm.";
command.HelpOption("-?|-h|--help");
var populationSizeOption = command.Option("-s|--size", "Specify population size", CommandOptionType.SingleValue);
var iterationsNumberOption = command.Option("-i|--iterations", "Specify number of iterations", CommandOptionType.SingleValue);
var mutationProbabilityOption = command.Option("-mp", "Specify probability of mutation (0<=p<=100)", CommandOptionType.SingleValue);
var jobTypeOption = command.Option("-js|--jobs-system", "Specify jobs system: \"f\", \"j\" or \"o\"", CommandOptionType.SingleValue);
var inputFileOption = command.Option("-if|--input-file", "Specify input file", CommandOptionType.SingleValue);
var saveResultOption = command.Option("-sr|--save-result", "Specify if save result", CommandOptionType.NoValue);
var executionsNumberOption = command.Option("-e|--executions", "Specify number of executions", CommandOptionType.SingleValue);
command.OnExecute(() => {
var populationSize = populationSizeOption.HasValue() ? Int16.Parse(populationSizeOption.Value()) : 10;
var iterationNumber = iterationsNumberOption.HasValue() ? Int16.Parse(iterationsNumberOption.Value()) : 5;
var mutationProbability = mutationProbabilityOption.HasValue() ? float.Parse(mutationProbabilityOption.Value()) : 1F;
var jobType = jobTypeOption.HasValue() ? jobTypeOption.Value() : "f";
var inputFile = inputFileOption.HasValue() ? inputFileOption.Value() : "TestInstances/flowshop/tai20_5/0.txt";
var saveResult = saveResultOption.HasValue() ? true : false;
var executions = executionsNumberOption.HasValue() ? Int16.Parse(executionsNumberOption.Value()) : 1;
var optionsString =
$" - population size:\t {populationSize}, {Environment.NewLine}"
+ $" - iterations number:\t {iterationNumber}. {Environment.NewLine}"
+ $" - mutation probability:\t {mutationProbability}. {Environment.NewLine}"
+ $" - job scheduling type:\t {jobType}, {Environment.NewLine}"
+ $" - input file:\t\t {inputFile} {Environment.NewLine}"
+ $" - save results:\t {saveResult} {Environment.NewLine}"
+ $" - executions number:\t {executions} {Environment.NewLine}";
Console.WriteLine($"{Environment.NewLine}"
+ $"Starting perform GA algorithm with given options: {Environment.NewLine}"
+ optionsString
);
var inputData = FileHelper.ReadFile(_inputDataDirectory, inputFile);
var schedulingProblem = SchedulingProblemFactory.Create(jobType, inputData);
var algorithm = new GeneticAlgorithm(iterationNumber, populationSize, schedulingProblem, mutationProbability);
var stringResult = optionsString.Replace("\t", "") + Environment.NewLine;
for (int i = 0; i < executions; i++)
{
if (executions > 0)
{
Console.WriteLine("Execution number: " + (i + 1));
}
var result = algorithm.Perform();
stringResult += Environment.NewLine + result.TimeSpan;
Console.WriteLine(Environment.NewLine + "Result: " + result.TimeSpan);
}
if (saveResult)
{
DateTime time = DateTime.Now;
string format = "dd.MM.yyyy_HH:mm:ss";
var fileName = "GA_" + time.ToString(format) + ".txt";
Console.WriteLine("Saving results to " + fileName);
FileHelper.CreateFile(_resultsDirectory, fileName, stringResult);
}
Console.WriteLine(Environment.NewLine + "Calculations finished.");
return(0);
});
});
app.Execute(args);
}
