// get solver from array of interval variables
private static Solver GetSolver(IntervalVar[] vars)
{
if (vars == null || vars.Length <= 0)
throw new ArgumentException("Array <vars> cannot be null or empty");
return vars[0].solver();
}
static void Main()
{
CpModel model = new CpModel();
int horizon = 100;
// C# code supports constant of affine expressions.
IntVar start_var = model.NewIntVar(0, horizon, "start");
IntVar end_var = model.NewIntVar(0, horizon, "end");
BoolVar presence_var = model.NewBoolVar("presence");
IntervalVar interval = model.NewOptionalIntervalVar(start_var, 10, end_var + 2, presence_var, "interval");
Console.WriteLine(interval);
// If the size is fixed, a simpler version uses the start expression, the size and the
// literal.
IntervalVar fixedSizeIntervalVar =
model.NewOptionalFixedSizeIntervalVar(start_var, 10, presence_var, "fixed_size_interval_var");
Console.WriteLine(fixedSizeIntervalVar);
// A fixed interval can be created using the same API.
IntervalVar fixedInterval = model.NewOptionalFixedSizeIntervalVar(5, 10, presence_var, "fixed_interval");
Console.WriteLine(fixedInterval);
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(int count, int[] start_min, int[] start_max, int[] duration,
bool optional, string name)
{
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i)
{
array[i] = MakeFixedDurationIntervalVar(start_min[i], start_max[i], duration[i], optional, name + i);
}
return(array);
}
static void Main()
{
CpModel model = new CpModel();
int horizon = 100;
IntVar start_var = model.NewIntVar(0, horizon, "start");
// C# code supports IntVar or integer constants in intervals.
int duration = 10;
IntVar end_var = model.NewIntVar(0, horizon, "end");
IntervalVar interval = model.NewIntervalVar(start_var, duration, end_var, "interval");
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(int count, long start_min, long start_max, long duration,
bool optional)
{
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i)
{
array[i] = MakeFixedDurationIntervalVar(start_min, start_max, duration, optional, "");
}
return(array);
}
static void Main()
{
InitTaskList();
int taskCount = GetTaskCount();
CpModel model = new CpModel();
IntervalVar[] tasks = new IntervalVar[taskCount];
BoolVar[] taskChoosed = new BoolVar[taskCount];
IntVar[] allEnds = new IntVar[GetEndTaskCount()];
int endJobCounter = 0;
foreach (Job j in myJobList)
{
BoolVar[] tmp = new BoolVar[j.AlternativeTasks.Count];
int i = 0;
foreach (Task t in j.AlternativeTasks)
{
long ti = taskIndexes[t.Name];
taskChoosed[ti] = model.NewBoolVar(t.Name + "_choose");
tmp[i++] = taskChoosed[ti];
IntVar start = model.NewIntVar(0, 10000, t.Name + "_start");
IntVar end = model.NewIntVar(0, 10000, t.Name + "_end");
tasks[ti] = model.NewIntervalVar(start, t.Duration, end, t.Name + "_interval");
if (j.Successor == null)
{
allEnds[endJobCounter++] = end;
}
if (!tasksToEquipment.ContainsKey(t.Equipment))
{
tasksToEquipment[t.Equipment] = new List <IntervalVar>();
}
tasksToEquipment[t.Equipment].Add(tasks[ti]);
}
model.AddExactlyOne(tmp);
}
foreach (KeyValuePair <long, List <IntervalVar> > pair in tasksToEquipment)
{
model.AddNoOverlap(pair.Value);
}
IntVar makespan = model.NewIntVar(0, 100000, "makespan");
model.AddMaxEquality(makespan, allEnds);
model.Minimize(makespan);
// Create the solver.
CpSolver solver = new CpSolver();
// Solve the problem.
solver.Solve(model);
Console.WriteLine(solver.ResponseStats());
}
static void Main()
{
CpModel model = new CpModel();
// Three weeks.
int horizon = 21;
// Task 0, duration 2.
IntVar start_0 = model.NewIntVar(0, horizon, "start_0");
int duration_0 = 2;
IntVar end_0 = model.NewIntVar(0, horizon, "end_0");
IntervalVar task_0 =
model.NewIntervalVar(start_0, duration_0, end_0, "task_0");
// Task 1, duration 4.
IntVar start_1 = model.NewIntVar(0, horizon, "start_1");
int duration_1 = 4;
IntVar end_1 = model.NewIntVar(0, horizon, "end_1");
IntervalVar task_1 =
model.NewIntervalVar(start_1, duration_1, end_1, "task_1");
// Task 2, duration 3.
IntVar start_2 = model.NewIntVar(0, horizon, "start_2");
int duration_2 = 3;
IntVar end_2 = model.NewIntVar(0, horizon, "end_2");
IntervalVar task_2 =
model.NewIntervalVar(start_2, duration_2, end_2, "task_2");
// Weekends.
IntervalVar weekend_0 = model.NewIntervalVar(5, 2, 7, "weekend_0");
IntervalVar weekend_1 = model.NewIntervalVar(12, 2, 14, "weekend_1");
IntervalVar weekend_2 = model.NewIntervalVar(19, 2, 21, "weekend_2");
// No Overlap constraint.
model.AddNoOverlap(new IntervalVar[] { task_0, task_1, task_2, weekend_0,
weekend_1, weekend_2 });
// Makespan objective.
IntVar obj = model.NewIntVar(0, horizon, "makespan");
model.AddMaxEquality(obj, new IntVar[] { end_0, end_1, end_2 });
model.Minimize(obj);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine("Optimal Schedule Length: " + solver.ObjectiveValue);
Console.WriteLine("Task 0 starts at " + solver.Value(start_0));
Console.WriteLine("Task 1 starts at " + solver.Value(start_1));
Console.WriteLine("Task 2 starts at " + solver.Value(start_2));
}
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(IntVar[] starts, long[] durations, string name)
{
int count = starts.Length;
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i)
{
array[i] = MakeFixedDurationIntervalVar(starts[i], durations[i], name + i);
}
return(array);
}
public void TestInterval()
{
Console.WriteLine("TestInterval test");
CpModel model = new CpModel();
IntVar v = model.NewIntVar(-10, 10, "v");
IntervalVar i = model.NewFixedSizeIntervalVar(v, 3, "i");
Assert.Equal("v", i.StartExpr().ShortString());
Assert.Equal("3", i.SizeExpr().ShortString());
Assert.Equal("(v + 3)", i.EndExpr().ShortString());
}
static void OptionalIntervalSample()
{
CpModel model = new CpModel();
int horizon = 100;
IntVar start_var = model.NewIntVar(0, horizon, "start");
// C# code supports IntVar or integer constants in intervals.
int duration = 10;
IntVar end_var = model.NewIntVar(0, horizon, "end");
IntVar presence_var = model.NewBoolVar("presence");
IntervalVar interval = model.NewOptionalIntervalVar(
start_var, duration, end_var, presence_var, "interval");
}
public Task(IntVar s, IntVar e, IntervalVar i, int oper, List <string> lots, string ent, string recipe, int paket, int prio, int maxOperAgo, int duration)
{
start = s;
end = e;
interval = i;
operation = oper;
this.lots = lots;
entity = ent;
this.recipe = recipe;
this.paket = paket;
priority = prio;
sortPriority = 0;
this.maxOperAgo = maxOperAgo;
this.duration = duration;
}
public static void JobShopCp(List <Job> jobs, int machines)
{
CpModel model = new CpModel();
int maxValue = 0;
foreach (var job in jobs)
{
maxValue += job.Tasks.Sum(j => j.Duration);
}
List <List <IntervalVar> > intervals = new List <List <IntervalVar> >(jobs.Count);
List <List <IntVar> > starts = new List <List <IntVar> >(jobs.Count);
List <List <IntVar> > ends = new List <List <IntVar> >(jobs.Count);
List <List <IntervalVar> > machinesIntervals = new List <List <IntervalVar> >();
List <List <IntVar> > machinesStarts = new List <List <IntVar> >();
for (int i = 0; i < machines; i++)
{
machinesIntervals.Add(new List <IntervalVar>());
machinesStarts.Add(new List <IntVar>());
}
foreach (var job in jobs)
{
starts.Add(new List <IntVar>());
ends.Add(new List <IntVar>());
intervals.Add(new List <IntervalVar>());
foreach (var task in job.Tasks)
{
IntVar start = model.NewIntVar(0, maxValue, job.Name + task.Name);
IntVar end = model.NewIntVar(0, maxValue, job.Name + task.Name);
IntervalVar oneTask =
model.NewIntervalVar(start, task.Duration, end, job.Name + task.Name);
intervals[job.JobId].Add(oneTask);
starts[job.JobId].Add(start);
ends[job.JobId].Add(end);
machinesIntervals[task.Machine].Add(oneTask);
machinesStarts[task.Machine].Add(start);
}
}
for (int j = 0; j < jobs.Count; ++j)
{
for (int t = 0; t < jobs[j].Tasks.Count - 1; ++t)
{
model.Add(ends[j][t] <= starts[j][t + 1]);
}
}
for (int machineId = 0; machineId < machines; ++machineId)
{
model.AddNoOverlap(machinesIntervals[machineId].ToArray());
}
IntVar[] allEnds = new IntVar[jobs.Count];
for (int i = 0; i < jobs.Count; i++)
{
allEnds[i] = ends[i].Last();
}
IntVar makespan = model.NewIntVar(0, maxValue, "makespan");
model.AddMaxEquality(makespan, allEnds);
model.Minimize(makespan);
CpSolver solver = new CpSolver();
solver.StringParameters = "max_time_in_seconds:30.0";
CpSolverStatus solverStatus = solver.Solve(model);
if (solverStatus != CpSolverStatus.Optimal)
{
Console.WriteLine("Solver didn’t find optimal solution!");
}
Console.WriteLine("Objective value = " + solver.Value(makespan));
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(int count,
int[] start_min,
int[] start_max,
int[] duration,
bool optional,
string name) {
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i) {
array[i] = MakeFixedDurationIntervalVar(start_min[i],
start_max[i],
duration[i],
optional,
name + i);
}
return array;
}
static void Main()
{
CpModel model = new CpModel();
// Three weeks.
int horizon = 100;
int num_tasks = 4;
IntVar[] starts = new IntVar[num_tasks];
IntVar[] ends = new IntVar[num_tasks];
IntervalVar[] intervals = new IntervalVar[num_tasks];
ILiteral[] presences = new ILiteral[num_tasks];
IntVar[] ranks = new IntVar[num_tasks];
IntVar true_var = model.NewConstant(1);
// Creates intervals, half of them are optional.
for (int t = 0; t < num_tasks; ++t)
{
starts[t] = model.NewIntVar(0, horizon, String.Format("start_{0}", t));
int duration = t + 1;
ends[t] = model.NewIntVar(0, horizon, String.Format("end_{0}", t));
if (t < num_tasks / 2)
{
intervals[t] = model.NewIntervalVar(starts[t], duration, ends[t],
String.Format("interval_{0}", t));
presences[t] = true_var;
}
else
{
presences[t] = model.NewBoolVar(String.Format("presence_{0}", t));
intervals[t] = model.NewOptionalIntervalVar(
starts[t], duration, ends[t], presences[t],
String.Format("o_interval_{0}", t));
}
// Ranks = -1 if and only if the tasks is not performed.
ranks[t] =
model.NewIntVar(-1, num_tasks - 1, String.Format("rank_{0}", t));
}
// Adds NoOverlap constraint.
model.AddNoOverlap(intervals);
// Adds ranking constraint.
RankTasks(model, starts, presences, ranks);
// Adds a constraint on ranks.
model.Add(ranks[0] < ranks[1]);
// Creates makespan variable.
IntVar makespan = model.NewIntVar(0, horizon, "makespan");
for (int t = 0; t < num_tasks; ++t)
{
model.Add(ends[t] <= makespan).OnlyEnforceIf(presences[t]);
}
// Minimizes makespan - fixed gain per tasks performed.
// As the fixed cost is less that the duration of the last interval,
// the solver will not perform the last interval.
IntVar[] presences_as_int_vars = new IntVar[num_tasks];
for (int t = 0; t < num_tasks; ++t)
{
presences_as_int_vars[t] = (IntVar)presences[t];
}
model.Minimize(2 * makespan - 7 * presences_as_int_vars.Sum());
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine(String.Format("Optimal cost: {0}",
solver.ObjectiveValue));
Console.WriteLine(String.Format("Makespan: {0}", solver.Value(makespan)));
for (int t = 0; t < num_tasks; ++t)
{
if (solver.BooleanValue(presences[t]))
{
Console.WriteLine(String.Format(
"Task {0} starts at {1} with rank {2}",
t, solver.Value(starts[t]), solver.Value(ranks[t])));
}
else
{
Console.WriteLine(String.Format(
"Task {0} in not performed and ranked at {1}", t,
solver.Value(ranks[t])));
}
}
}
else
{
Console.WriteLine(
String.Format("Solver exited with nonoptimal status: {0}", status));
}
}
static void Main()
{
CpModel model = new CpModel();
int[,] jobs = new[, ] {
{ 3, 3 }, { 2, 5 }, { 1, 3 }, { 3, 7 }, { 7, 3 }, { 2, 2 }, { 2, 2 }, { 5, 5 },
{ 10, 2 }, { 4, 3 }, { 2, 6 }, { 1, 2 }, { 6, 8 }, { 4, 5 }, { 3, 7 }
};
int max_length = 10;
int num_jobs = jobs.GetLength(0);
var all_jobs = Enumerable.Range(0, num_jobs);
int horizon = 0;
foreach (int j in all_jobs)
{
horizon += jobs[j, 0];
}
List <IntervalVar> intervals = new List <IntervalVar>();
List <IntervalVar> intervals0 = new List <IntervalVar>();
List <IntervalVar> intervals1 = new List <IntervalVar>();
List <IntVar> performed = new List <IntVar>();
List <IntVar> starts = new List <IntVar>();
List <IntVar> ends = new List <IntVar>();
List <int> demands = new List <int>();
foreach (int i in all_jobs)
{
// Create main interval.
IntVar start = model.NewIntVar(0, horizon, String.Format("start_{0}", i));
int duration = jobs[i, 0];
IntVar end = model.NewIntVar(0, horizon, String.Format("end_{0}", i));
IntervalVar interval = model.NewIntervalVar(start, duration, end, String.Format("interval_{0}", i));
starts.Add(start);
intervals.Add(interval);
ends.Add(end);
demands.Add(jobs[i, 1]);
IntVar performed_on_m0 = model.NewBoolVar(String.Format("perform_{0}_on_m0", i));
performed.Add(performed_on_m0);
// Create an optional copy of interval to be executed on machine 0.
IntVar start0 = model.NewIntVar(0, horizon, String.Format("start_{0}_on_m0", i));
IntVar end0 = model.NewIntVar(0, horizon, String.Format("end_{0}_on_m0", i));
IntervalVar interval0 = model.NewOptionalIntervalVar(start0, duration, end0, performed_on_m0,
String.Format("interval_{0}_on_m0", i));
intervals0.Add(interval0);
// Create an optional copy of interval to be executed on machine 1.
IntVar start1 = model.NewIntVar(0, horizon, String.Format("start_{0}_on_m1", i));
IntVar end1 = model.NewIntVar(0, horizon, String.Format("end_{0}_on_m1", i));
IntervalVar interval1 = model.NewOptionalIntervalVar(start1, duration, end1, performed_on_m0.Not(),
String.Format("interval_{0}_on_m1", i));
intervals1.Add(interval1);
// We only propagate the constraint if the tasks is performed on the
// machine.
model.Add(start0 == start).OnlyEnforceIf(performed_on_m0);
model.Add(start1 == start).OnlyEnforceIf(performed_on_m0.Not());
}
// Max Length constraint (modeled as a cumulative)
model.AddCumulative(intervals, demands, max_length);
// Choose which machine to perform the jobs on.
model.AddNoOverlap(intervals0);
model.AddNoOverlap(intervals1);
// Objective variable.
IntVar makespan = model.NewIntVar(0, horizon, "makespan");
model.AddMaxEquality(makespan, ends);
model.Minimize(makespan);
// Symmetry breaking.
model.Add(performed[0] == 0);
// Creates the solver and solve.
CpSolver solver = new CpSolver();
solver.Solve(model);
// Output solution.
Console.WriteLine("Solution");
Console.WriteLine(" - makespan = " + solver.ObjectiveValue);
foreach (int i in all_jobs)
{
long performed_machine = 1 - solver.Value(performed[i]);
long start = solver.Value(starts[i]);
Console.WriteLine(String.Format(" - Job {0} starts at {1} on machine {2}", i, start, performed_machine));
}
Console.WriteLine("Statistics");
Console.WriteLine(" - conflicts : " + solver.NumConflicts());
Console.WriteLine(" - branches : " + solver.NumBranches());
Console.WriteLine(" - wall time : " + solver.WallTime() + " ms");
}
// [END assigned_task]
public static void Main(String[] args)
{
// [START data]
var allJobs =
new[] {
new[] {
// job0
new { machine = 0, duration = 3 }, // task0
new { machine = 1, duration = 2 }, // task1
new { machine = 2, duration = 2 }, // task2
}
.ToList(),
new[] {
// job1
new { machine = 0, duration = 2 }, // task0
new { machine = 2, duration = 1 }, // task1
new { machine = 1, duration = 4 }, // task2
}
.ToList(),
new[] {
// job2
new { machine = 1, duration = 4 }, // task0
new { machine = 2, duration = 3 }, // task1
}
.ToList(),
}
.ToList();
int numMachines = 0;
foreach (var job in allJobs)
{
foreach (var task in job)
{
numMachines = Math.Max(numMachines, 1 + task.machine);
}
}
int[] allMachines = Enumerable.Range(0, numMachines).ToArray();
// Computes horizon dynamically as the sum of all durations.
int horizon = 0;
foreach (var job in allJobs)
{
foreach (var task in job)
{
horizon += task.duration;
}
}
// [END data]
// Creates the model.
// [START model]
CpModel model = new CpModel();
// [END model]
// [START variables]
Dictionary <Tuple <int, int>, Tuple <IntVar, IntVar, IntervalVar> > allTasks =
new Dictionary <Tuple <int, int>, Tuple <IntVar, IntVar, IntervalVar> >(); // (start, end, duration)
Dictionary <int, List <IntervalVar> > machineToIntervals = new Dictionary <int, List <IntervalVar> >();
for (int jobID = 0; jobID < allJobs.Count(); ++jobID)
{
var job = allJobs[jobID];
for (int taskID = 0; taskID < job.Count(); ++taskID)
{
var task = job[taskID];
String suffix = $"_{jobID}_{taskID}";
IntVar start = model.NewIntVar(0, horizon, "start" + suffix);
IntVar end = model.NewIntVar(0, horizon, "end" + suffix);
IntervalVar interval = model.NewIntervalVar(start, task.duration, end, "interval" + suffix);
var key = Tuple.Create(jobID, taskID);
allTasks[key] = Tuple.Create(start, end, interval);
if (!machineToIntervals.ContainsKey(task.machine))
{
machineToIntervals.Add(task.machine, new List <IntervalVar>());
}
machineToIntervals[task.machine].Add(interval);
}
}
// [END variables]
// [START constraints]
// Create and add disjunctive constraints.
foreach (int machine in allMachines)
{
model.AddNoOverlap(machineToIntervals[machine]);
}
// Precedences inside a job.
for (int jobID = 0; jobID < allJobs.Count(); ++jobID)
{
var job = allJobs[jobID];
for (int taskID = 0; taskID < job.Count() - 1; ++taskID)
{
var key = Tuple.Create(jobID, taskID);
var nextKey = Tuple.Create(jobID, taskID + 1);
model.Add(allTasks[nextKey].Item1 >= allTasks[key].Item2);
}
}
// [END constraints]
// [START objective]
// Makespan objective.
IntVar objVar = model.NewIntVar(0, horizon, "makespan");
List <IntVar> ends = new List <IntVar>();
for (int jobID = 0; jobID < allJobs.Count(); ++jobID)
{
var job = allJobs[jobID];
var key = Tuple.Create(jobID, job.Count() - 1);
ends.Add(allTasks[key].Item2);
}
model.AddMaxEquality(objVar, ends);
model.Minimize(objVar);
// [END objective]
// Solve
// [START solve]
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
Console.WriteLine($"Solve status: {status}");
// [END solve]
// [START print_solution]
if (status == CpSolverStatus.Optimal || status == CpSolverStatus.Feasible)
{
Console.WriteLine("Solution:");
Dictionary <int, List <AssignedTask> > assignedJobs = new Dictionary <int, List <AssignedTask> >();
for (int jobID = 0; jobID < allJobs.Count(); ++jobID)
{
var job = allJobs[jobID];
for (int taskID = 0; taskID < job.Count(); ++taskID)
{
var task = job[taskID];
var key = Tuple.Create(jobID, taskID);
int start = (int)solver.Value(allTasks[key].Item1);
if (!assignedJobs.ContainsKey(task.machine))
{
assignedJobs.Add(task.machine, new List <AssignedTask>());
}
assignedJobs[task.machine].Add(new AssignedTask(jobID, taskID, start, task.duration));
}
}
// Create per machine output lines.
String output = "";
foreach (int machine in allMachines)
{
// Sort by starting time.
assignedJobs[machine].Sort();
String solLineTasks = $"Machine {machine}: ";
String solLine = " ";
foreach (var assignedTask in assignedJobs[machine])
{
String name = $"job_{assignedTask.jobID}_task_{assignedTask.taskID}";
// Add spaces to output to align columns.
solLineTasks += $"{name,-15}";
String solTmp = $"[{assignedTask.start},{assignedTask.start+assignedTask.duration}]";
// Add spaces to output to align columns.
solLine += $"{solTmp,-15}";
}
output += solLineTasks + "\n";
output += solLine + "\n";
}
// Finally print the solution found.
Console.WriteLine($"Optimal Schedule Length: {solver.ObjectiveValue}");
Console.WriteLine($"\n{output}");
}
else
{
Console.WriteLine("No solution found.");
}
// [END print_solution]
// [START statistics]
Console.WriteLine("Statistics");
Console.WriteLine($" conflicts: {solver.NumConflicts()}");
Console.WriteLine($" branches : {solver.NumBranches()}");
Console.WriteLine($" wall time: {solver.WallTime()}s");
// [END statistics]
}
private void Model()
{
/* Building basic task data structures */
for (int i = 0; i < tasks.Length; i++)
{
/* Create a new set of possible IntervalVars & IntVar to decide
* which one (and only 1) is performed */
taskStructures[i] = new TaskAlternative(tasks[i]);
/* Container to use when posting constraints */
location2Task[tasks[i].LocationId][tasks[i].TaskPosition] = taskStructures[i];
/* Get task type */
int taskType = tasks[i].TaskType;
/* Possible tool for this task */
List <Tool> tools = taskType2Tool[taskType];
bool optional = tools.Count > 1;
/* List of boolean variables. If performedOnTool[t] == true then
* the task is performed on tool t */
List <IntVar> performedOnTool = new List <IntVar>();
for (int t = 0; t < tools.Count; t++)
{
/* Creating an IntervalVar. If tools.Count > 1 the intervalVar
* is *OPTIONAL* */
int toolId = tools[t].Id;
Debug.Assert(tasks[i].Durations.ContainsKey(toolId));
int duration = tasks[i].Durations[toolId];
string name = "J " + tasks[i].Id + " [" + toolId + "]";
IntervalVar intervalVar;
if (taskType == factoryData.Inspection)
{
/* We set a 0 time if the task is an inspection */
duration = 0;
intervalVar = solver.MakeFixedDurationIntervalVar(0, horizon, duration, optional, name);
IntVar start = intervalVar.SafeStartExpr(-1).Var();
intervalVar.SafeStartExpr(-1).Var().SetValues(factoryData.InspectionStarts);
}
else
{
intervalVar = solver.MakeFixedDurationIntervalVar(0, horizon, duration, optional, name);
}
taskStructures[i].Intervals.Add(intervalVar);
tool2Task[toolId].Add(intervalVar);
toolIntervalVar2TaskId[toolId].Add(i);
/* Collecting all the bool vars, even if they are optional */
performedOnTool.Add(intervalVar.PerformedExpr().Var());
}
/* Linking the bool var to a single integer variable: */
/* if alternativeToolVar == t <=> performedOnTool[t] == true */
string alternativeName = "J " + tasks[i].Id;
IntVar alternativeToolVar = solver.MakeIntVar(0, tools.Count - 1, alternativeName);
taskStructures[i].ToolVar = alternativeToolVar;
solver.Add(solver.MakeMapDomain(alternativeToolVar, performedOnTool.ToArray()));
Debug.Assert(performedOnTool.ToArray().Length == alternativeToolVar.Max() + 1);
selectedTool.Add(alternativeToolVar);
}
/* Creates precedences on a work Location in order to enforce a
* fully ordered set within the same location
*/
for (int d = 0; d < location2Task.Length; d++)
{
for (int i = 0; i < location2Task[d].Length - 1; i++)
{
TaskAlternative task1 = location2Task[d][i];
TaskAlternative task2 = location2Task[d][i + 1];
/* task1 must end before task2 starts */
/* Adding precedence for each possible alternative pair */
for (int t1 = 0; t1 < task1.Intervals.Count(); t1++)
{
IntervalVar task1Alternative = task1.Intervals[t1];
for (int t2 = 0; t2 < task2.Intervals.Count(); t2++)
{
IntervalVar task2Alternative = task2.Intervals[t2];
Constraint precedence = solver.MakeIntervalVarRelation(
task2Alternative, Solver.STARTS_AFTER_END, task1Alternative);
solver.Add(precedence);
}
}
}
}
/* Adds disjunctive constraints on unary resources, and creates
* sequence variables. */
for (int t = 0; t < factoryData.NbTools; t++)
{
string name = "Tool " + t;
if (!factoryData.Tools[t].CanPerformTaskType(factoryData.Inspection))
{
DisjunctiveConstraint ct = solver.MakeDisjunctiveConstraint(tool2Task[t].ToArray(), name);
solver.Add(ct);
allToolSequences[t] = ct.SequenceVar();
}
PostTransitionTimeConstraints(t, true);
}
/* Collecting all tasks end for makespan objective function */
List <IntVar> intervalEnds = new List <IntVar>();
for (int i = 0; i < tasks.Length; i++)
{
foreach (IntervalVar var in taskStructures[i].Intervals)
{
intervalEnds.Add(var.SafeEndExpr(-1).Var());
}
}
/* Objective: minimize the makespan (maximum end times of all tasks) */
makespan = solver.MakeMax(intervalEnds.ToArray()).Var();
objective = solver.MakeMinimize(makespan, 1);
}
static void Main()
{
int[,] durations = new int[, ] {
{ 1, 3, 6, 7, 3, 6 }, { 8, 5, 10, 10, 10, 4 }, { 5, 4, 8, 9, 1, 7 },
{ 5, 5, 5, 3, 8, 9 }, { 9, 3, 5, 4, 3, 1 }, { 3, 3, 9, 10, 4, 1 }
};
int[,] machines = new int[, ] {
{ 2, 0, 1, 3, 5, 4 }, { 1, 2, 4, 5, 0, 3 }, { 2, 3, 5, 0, 1, 4 },
{ 1, 0, 2, 3, 4, 5 }, { 2, 1, 4, 5, 0, 3 }, { 1, 3, 5, 0, 4, 2 }
};
int num_jobs = durations.GetLength(0);
int num_machines = durations.GetLength(1);
var all_jobs = Enumerable.Range(0, num_jobs);
var all_machines = Enumerable.Range(0, num_machines);
int horizon = 0;
foreach (int j in all_jobs)
{
foreach (int m in all_machines)
{
horizon += durations[j, m];
}
}
// Creates the model.
CpModel model = new CpModel();
// Creates jobs.
Task[,] all_tasks = new Task[num_jobs, num_machines];
foreach (int j in all_jobs)
{
foreach (int m in all_machines)
{
IntVar start_var = model.NewIntVar(0, horizon, String.Format("start_{0}_{1}", j, m));
int duration = durations[j, m];
IntVar end_var = model.NewIntVar(0, horizon, String.Format("end_{0}_{1}", j, m));
IntervalVar interval_var =
model.NewIntervalVar(start_var, duration, end_var, String.Format("interval_{0}_{1}", j, m));
all_tasks[j, m] = new Task(start_var, end_var, interval_var);
}
}
// Create disjuctive constraints.
List <IntervalVar>[] machine_to_jobs = new List <IntervalVar> [num_machines];
foreach (int m in all_machines)
{
machine_to_jobs[m] = new List <IntervalVar>();
}
foreach (int j in all_jobs)
{
foreach (int m in all_machines)
{
machine_to_jobs[machines[j, m]].Add(all_tasks[j, m].interval);
}
}
foreach (int m in all_machines)
{
model.AddNoOverlap(machine_to_jobs[m]);
}
// Precedences inside a job.
foreach (int j in all_jobs)
{
for (int k = 0; k < num_machines - 1; ++k)
{
model.Add(all_tasks[j, k + 1].start >= all_tasks[j, k].end);
}
}
// Makespan objective.
IntVar[] all_ends = new IntVar[num_jobs];
foreach (int j in all_jobs)
{
all_ends[j] = all_tasks[j, num_machines - 1].end;
}
IntVar makespan = model.NewIntVar(0, horizon, "makespan");
model.AddMaxEquality(makespan, all_ends);
model.Minimize(makespan);
Console.WriteLine(model.ModelStats());
// Creates the solver and solve.
CpSolver solver = new CpSolver();
// Display a few solutions picked at random.
solver.Solve(model);
// Statistics.
Console.WriteLine(solver.ResponseStats());
}
public void RunJobShopScheduling(String solverType)
{
Solver solver = new Solver(solverType);
if (solver == null)
{
Console.WriteLine("JobShop failed to create a solver " + solverType);
return;
}
// All tasks
Dictionary <string, IntervalVar> allTasks = new Dictionary <string, IntervalVar>();
// Creates jobs
for (int i = 0; i < allJobs.Count; i++)
{
for (int j = 0; j < machines.ElementAt(i).Count; j++)
{
IntervalVar oneTask = solver.MakeFixedDurationIntervalVar(0,
horizon,
processingTimes.ElementAt(i).ElementAt(j),
false,
"Job_" + i + "_" + j);
//Console.WriteLine("Job_" + i + "_" + j);
allTasks.Add("Job_" + i + "_" + j, oneTask);
}
}
// Create sequence variables and add disjuctive constraints
List <SequenceVar> allSequences = new List <SequenceVar>();
foreach (var machine in allMachines)
{
List <IntervalVar> machinesJobs = new List <IntervalVar>();
for (int i = 0; i < allJobs.Count; i++)
{
for (int k = 0; k < machines.ElementAt(i).Count; k++)
{
if (machines.ElementAt(i).ElementAt(k) == machine)
{
machinesJobs.Add(allTasks["Job_" + i + "_" + k]);
}
}
}
DisjunctiveConstraint disj = solver.MakeDisjunctiveConstraint(machinesJobs.ToArray(),
"machine " + machine);
allSequences.Add(disj.SequenceVar());
solver.Add(disj);
}
// Add conjunctive constraints
foreach (var job in allJobs)
{
for (int j = 0; j < machines.ElementAt(job).Count - 1; j++)
{
solver.Add(allTasks["Job_" + job + "_" + (j + 1)].StartsAfterEnd(allTasks["Job_" + job + "_" + j]));
}
}
// Set the objective
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < allJobs.Count; i++)
{
allEnds[i] = allTasks["Job_" + i + "_" + (machines.ElementAt(i).Count - 1)].EndExpr().Var();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(allEnds).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// Create search phases
DecisionBuilder sequencePhase = solver.MakePhase(allSequences.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder varsPhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objectiveVariable).
DecisionBuilder mainPhase = solver.Compose(sequencePhase, varsPhase);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(allSequences.ToArray());
collector.Add(objectiveVar);
foreach (var i in allMachines)
{
SequenceVar sequence = allSequences.ElementAt(i);
long sequenceCount = sequence.Size();
for (int j = 0; j < sequenceCount; j++)
{
IntervalVar t = sequence.Interval(j);
collector.Add(t.StartExpr().Var());
collector.Add(t.EndExpr().Var());
}
}
// Search.
bool solutionFound = solver.Solve(mainPhase, null, objectiveMonitor, null, collector);
if (solutionFound)
{
//The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
string solLine = "";
string solLineTasks = "";
Console.WriteLine("Time Intervals for Tasks\n");
List <List <TimeSpan> > tuplesSolution = new List <List <TimeSpan> >();
for (int m = 0; m < this.machinesCount; m++)
{
//Console.WriteLine("MachineCount: " + this.machinesCount);
solLine = "Machine " + m + " :";
solLineTasks = "Machine " + m + ": ";
SequenceVar seq = allSequences.ElementAt(m);
int[] storedSequence = collector.ForwardSequence(SOLUTION_INDEX, seq);
foreach (int taskIndex in storedSequence)
{
//Console.WriteLine("taskIndex: " + taskIndex);
IntervalVar task = seq.Interval(taskIndex);
solLineTasks += task.Name() + " ";
//Console.WriteLine("Len: " + storedSequence.Length);
}
// First GreenTime
//TimeSpan timeToAdd = tuplesSolution.First().Last();
TimeSpan timeEndBucket = this.bucketInfo.EndBucket;
TimeSpan timeStartBucket = this.bucketInfo.StartBucket;
int solutionSize = tuplesSolution.Count;
bool isEnd = false;
List <int> list_id = jobIds.ElementAt(m);
// Adding GreenTime to Solution
while (timeStartBucket.CompareTo(timeEndBucket) < 0)
{
foreach (int taskIndex in storedSequence)
{
IntervalVar task = seq.Interval(taskIndex);
var startValue = TimeSpan.FromSeconds(collector.Value(0, task.StartExpr().Var()));
var endValue = TimeSpan.FromSeconds(collector.Value(0, task.EndExpr().Var()));
TimeSpan greenTime = endValue.Subtract(startValue);
TimeSpan timeEnd;
timeEnd = timeStartBucket.Add(greenTime);
List <TimeSpan> tuple = new List <TimeSpan>();
tuple.Add(timeStartBucket);
if (timeEndBucket.CompareTo(timeEnd) < 0)
{
timeEnd = timeEndBucket;
isEnd = true;
}
tuple.Add(timeEnd);
tuplesSolution.Add(tuple);
if (taskIndex + 1 < list_id.Count() && list_id.ElementAt(taskIndex) == list_id.ElementAt(taskIndex + 1))
{
timeStartBucket = timeStartBucket.Add(TimeSpan.FromSeconds(this.cycleTime));
}
else
{
timeStartBucket = timeEnd;
}
if (isEnd)
{
break;
}
}
}
//
// Saving the Solution to a XML file
//
JobShop.save(m, tuplesSolution);
//solLine += "\n";
//solLineTasks += "\n";
//Console.WriteLine(solLineTasks);
//Console.WriteLine(solLine);
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public static void Main(String[] args)
{
InitTaskList();
Solver solver = new Solver("Jobshop");
// ----- Creates all Intervals and vars -----
// All tasks
List <IntervalVar> allTasks = new List <IntervalVar>();
// Stores all tasks attached interval variables per job.
List <List <IntervalVar> >
jobsToTasks = new List <List <IntervalVar> >(jobsCount);
// machinesToTasks stores the same interval variables as above, but
// grouped my machines instead of grouped by jobs.
List <List <IntervalVar> >
machinesToTasks = new List <List <IntervalVar> >(machinesCount);
for (int i = 0; i < machinesCount; i++)
{
machinesToTasks.Add(new List <IntervalVar>());
}
// Creates all individual interval variables.
foreach (List <Task> job in myJobList)
{
jobsToTasks.Add(new List <IntervalVar>());
foreach (Task task in job)
{
IntervalVar oneTask = solver.MakeFixedDurationIntervalVar(
0, horizon, task.Duration, false, task.Name);
jobsToTasks[task.JobId].Add(oneTask);
allTasks.Add(oneTask);
machinesToTasks[task.Machine].Add(oneTask);
}
}
// ----- Creates model -----
// Creates precedences inside jobs.
foreach (List <IntervalVar> jobToTask in jobsToTasks)
{
int tasksCount = jobToTask.Count;
for (int task_index = 0; task_index < tasksCount - 1; ++task_index)
{
IntervalVar t1 = jobToTask[task_index];
IntervalVar t2 = jobToTask[task_index + 1];
Constraint prec =
solver.MakeIntervalVarRelation(t2, Solver.STARTS_AFTER_END, t1);
solver.Add(prec);
}
}
// Adds disjunctive constraints on unary resources, and creates
// sequence variables. A sequence variable is a dedicated variable
// whose job is to sequence interval variables.
SequenceVar[] allSequences = new SequenceVar[machinesCount];
for (int machineId = 0; machineId < machinesCount; ++machineId)
{
string name = "Machine_" + machineId;
DisjunctiveConstraint ct =
solver.MakeDisjunctiveConstraint(machinesToTasks[machineId].ToArray(),
name);
solver.Add(ct);
allSequences[machineId] = ct.SequenceVar();
}
// Creates array of end_times of jobs.
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < jobsCount; i++)
{
IntervalVar task = jobsToTasks[i].Last();
allEnds[i] = task.EndExpr().Var();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(allEnds).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// ----- Search monitors and decision builder -----
// This decision builder will rank all tasks on all machines.
DecisionBuilder sequencePhase =
solver.MakePhase(allSequences, Solver.SEQUENCE_DEFAULT);
// After the ranking of tasks, the schedule is still loose and any
// task can be postponed at will. But, because the problem is now a PERT
// (http://en.wikipedia.org/wiki/Program_Evaluation_and_Review_Technique),
// we can schedule each task at its earliest start time. This iscs
// conveniently done by fixing the objective variable to its
// minimum value.
DecisionBuilder objPhase = solver.MakePhase(
objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objectiveVariable).
DecisionBuilder mainPhase = solver.Compose(sequencePhase, objPhase);
// Search log.
const int kLogFrequency = 1000000;
SearchMonitor searchLog =
solver.MakeSearchLog(kLogFrequency, objectiveMonitor);
SearchLimit limit = null;
if (timeLimitInMs > 0)
{
limit = solver.MakeTimeLimit(timeLimitInMs);
}
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(allSequences);
collector.Add(allTasks.ToArray());
// Search.
bool solutionFound = solver.Solve(mainPhase, searchLog, objectiveMonitor,
limit, collector);
if (solutionFound)
{
//The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
for (int m = 0; m < machinesCount; ++m)
{
Console.WriteLine("Machine " + m + " :");
SequenceVar seq = allSequences[m];
int[] storedSequence = collector.ForwardSequence(SOLUTION_INDEX, seq);
foreach (int taskIndex in storedSequence)
{
IntervalVar task = seq.Interval(taskIndex);
long startMin = solution.StartMin(task);
long startMax = solution.StartMax(task);
if (startMin == startMax)
{
Console.WriteLine("Task " + task.Name() + " starts at " +
startMin + ".");
}
else
{
Console.WriteLine("Task " + task.Name() + " starts between " +
startMin + " and " + startMax + ".");
}
}
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public EntityHistory[] GetSolvedTasks(LotInfoJSON[] data, string startDate, int addMinutes)
{
List <string> diffList = new List <string>()
{
"LPN01", "LPN02", "LPN04", "LPP02", "LPP05", "FHT01", "LPT01", "FOX01", "FOX04", "FAL01"
};
Funcs funcs = new Funcs();
var date = funcs.StrToDate(startDate);
//если передано addMinutes == 25 часов то имеется в виду текущее время
if (addMinutes == 25 * 60)
{
addMinutes = 0;
date = DateTime.Now;
}
//Горизонт событий
int horizon = 48 * 60 * 100000;
// Creates the model.
CpModel model = new CpModel();
// Creates jobs.
List <Task> all_tasks = new List <Task>();
//Формирование задач для Солвера
foreach (string ent in diffList)
{
//Задачи одной установки
List <LotTask> entityTasks = new List <LotTask>();
foreach (var lot in data)
{
foreach (var task in lot.Tasks)
{
if (task.Entity == ent)
{
entityTasks.Add(task);
}
}
}
//Удаляем повторения задач
entityTasks = entityTasks.Distinct(new DistinctItemComparer()).ToList();
// Добавляем/дополняем задачи для Солвера
foreach (LotTask t in entityTasks)
{
IntVar start_var = model.NewIntVar(t.Delay, horizon, "Task Start");
int duration = t.Duration;
IntVar end_var = model.NewIntVar(0, horizon, "Task End");
IntervalVar interval_var = model.NewIntervalVar(
start_var, duration, end_var,
String.Join(' ', getLotsFromPaket(data, t.paket)));
//String.Format(getLotsFromPaket(data, t.paket) + t.Operation.ToString() + " " + t.Recipe + " " + t.Duration.ToString() + " " + t.Delay));
all_tasks.Add(new Task(start_var, end_var, interval_var, t.Operation, getLotsFromPaket(data, t.paket),
t.Entity, t.Recipe, t.paket, getPriority(data, t.Lot), getMaxOperAgo(data, t.Lot), t.Duration));
}
}
#region Constraints (ограничения, МВХ и условия)
/*
* // Последовательность запуска задач (согласно № операции) для одной партии
* // НЕ ВСЕГДА ВЕРНО!!!
* foreach(var lot in data.Select(x => x.Lot).ToList())
* {
* var tasks = all_tasks.Where(t => t.lots[0] == lot).ToList().OrderBy(t => t.operation).ToList();
*
* for (int t = 1; t < tasks.Count; t++)
* model.Add(tasks[t].start >= tasks[t - 1].end);
*
* }*/
//Назначение приоритетов сортировки по OperAgo & priority
foreach (var ent in diffList)
{
var allEntTasks = all_tasks.Where(e => e.entity == ent).ToList();
if (allEntTasks.Count > 1)
{
foreach (Task task in allEntTasks)
{
task.sortPriority = -task.maxOperAgo * 2 + task.priority * 3; // формула расчёта баллов приоритета
}
allEntTasks = allEntTasks.OrderByDescending(x => x.sortPriority).ToList(); // чем больше приоритет тем раньше старт
for (var t0 = 0; t0 < allEntTasks.Count - 1; t0++)
{
model.Add(allEntTasks[t0].start < allEntTasks[t0 + 1].start);
}
}
}
//МВХ захардкожено
foreach (var lot in data.Select(x => x.Lot).ToList())
{
var tasks = all_tasks.Where(t => t.lots[0] == lot).ToList().OrderBy(t => t.operation).ToList();
var mbx = 14;
for (int t = 1; t < tasks.Count; t++)
{
if (tasks[t].operation == 1015)
{
mbx = 14;
}
if (tasks[t].operation == 1120)
{
mbx = 14;
}
if (tasks[t].operation == 1350)
{
mbx = 24;
}
if (tasks[t].operation == 1380)
{
mbx = 24;
}
if (tasks[t].operation == 1390)
{
mbx = 24;
}
if (tasks[t].operation == 2280)
{
mbx = 24;
}
if (tasks[t].operation == 2292)
{
mbx = 14;
}
if (tasks[t].operation == 2390)
{
mbx = 14;
}
if (tasks[t].operation == 2410)
{
mbx = 8;
}
if (tasks[t].operation == 3270)
{
mbx = 14;
}
if (tasks[t].operation == 3280)
{
mbx = 24;
}
if (tasks[t].operation == 3390)
{
mbx = 14;
}
if (tasks[t].operation == 3410)
{
mbx = 24;
}
if (tasks[t].operation == 4520)
{
mbx = 24;
}
if (tasks[t].operation == 4560)
{
mbx = 72;
}
if (tasks[t].operation == 4960)
{
mbx = 24;
}
if (tasks[t].operation == 4990)
{
mbx = 72;
}
model.Add(tasks[t].start - tasks[t - 1].end <= mbx); // МВХ 14 часов
}
}
//Ограничение на продувки для FOX01
//Продувки после процесса: После каждого процесса OXIDE16T, OXIDE58TM, OXIDE581TM, OX533TM,
// OX531TM, OXIDE70TM, OXPO01C (АЛЬТЕРНАТИВНЫЙ) продувка WET-DCE-CLN - 280 мин
var FOX01Tasks = all_tasks.Where(t => t.entity == "FOX01").ToList();
List <string> FOX01Recipies = new List <string>()
{
"OXIDE16T", "OXIDE58TM", "OXIDE581TM", "OX533TM", "OX531TM", "OXIDE70TM", "OXPO01C"
};
for (int t = 0; t < FOX01Tasks.Count; t++)
{
if (FOX01Recipies.Contains(FOX01Tasks[t].recipe))
{
IntVar start_var = model.NewIntVar(0, horizon, "Purge Start");
int duration = 280;
IntVar end_var = model.NewIntVar(0, horizon, "Purge End");
IntervalVar interval_var = model.NewIntervalVar(start_var, duration, end_var, "After " + FOX01Tasks[t].recipe + " WET-DCE-CLN 280 min");
var purgeTask = new Task(start_var, end_var, interval_var, 0, new List <string>()
{
"FOX01 Purge"
}, "FOX01", "WET-DCE-CLN", 0, -1, 0, 280);
all_tasks.Add(purgeTask);
//model.Add(purgeTask.start >= FOX01Tasks[t].end);
model.Add(purgeTask.start - FOX01Tasks[t].end < 10);
model.Add(purgeTask.start - FOX01Tasks[t].end > 0);
}
;
}
//Ограничение на продувки для LPN02
// До и после обработки NIT1100A, NIT1500A, NITR1500K, NITR01 должна проходить
// ОБЯЗАТЕЛЬНО необходимая технологическая продувка 20-CYCLE-PURGE = 200 мин
var LPN02Tasks = all_tasks.Where(t => t.entity == "LPN02").ToList();
List <string> LPN02Recipies = new List <string>()
{
"NIT1100A", "NIT1500A", "NITR1500K", "NITR01"
};
//ограничение на продувку после рецепта
for (int t = 0; t < LPN02Tasks.Count; t++)
{
if (LPN02Recipies.Contains(LPN02Tasks[t].recipe))
{
IntVar start_var = model.NewIntVar(0, horizon, "Purge Start");
IntVar end_var = model.NewIntVar(0, horizon, "Purge End");
IntervalVar interval_var = model.NewIntervalVar(start_var, 200, end_var, "After " + LPN02Tasks[t].recipe + " 20-CYCLE-PURGE 200 min");
var purgeTaskAfter = new Task(start_var, end_var, interval_var, 0, new List <string> {
"Purge After"
}, "LPN02", "20-CYCLE-PURGE", 0, -1, 0, 200);
all_tasks.Add(purgeTaskAfter);
model.Add(purgeTaskAfter.start - LPN02Tasks[t].end < 10);
model.Add(purgeTaskAfter.start - LPN02Tasks[t].end > 0);
}
;
}
//ограничение на продувку до рецепта
for (int t = 0; t < LPN02Tasks.Count; t++)
{
if (LPN02Recipies.Contains(LPN02Tasks[t].recipe))
{
IntVar start_var = model.NewIntVar(0, horizon, "Purge Start");
IntVar end_var = model.NewIntVar(0, horizon, "Purge End");
IntervalVar interval_var = model.NewIntervalVar(start_var, 200, end_var, "Before " + LPN02Tasks[t].recipe + " 20-CYCLE-PURGE 200 min");
var purgeTaskBefore = new Task(start_var, end_var, interval_var, 0, new List <string> {
"Purge Before"
}, "LPN02", "20-CYCLE-PURGE", 0, -1, 0, 200);
all_tasks.Add(purgeTaskBefore);
model.Add(LPN02Tasks[t].start - purgeTaskBefore.end < 10);
model.Add(LPN02Tasks[t].start - purgeTaskBefore.end > 0);
}
;
}
//!!!НУЖНО СДЕЛАТЬ!!! Ограничение: запрет 2-х продувок подряд !!!НУЖНО СДЕЛАТЬ!!!
//var tasksBefore = all_tasks.Where(b => b.lot == "Purge Before").ToList();
//var tasksAfter = all_tasks.Where(a => a.lot == "Purge After").ToList();
// Ограничение на отсутствие пересечений задач для одной установки
foreach (var ent in diffList)
{
List <IntervalVar> machine_to_jobs = new List <IntervalVar>();
var entTasks = all_tasks.Where(t => (t.entity == ent)).ToList();
for (int j = 0; j < entTasks.Count; j++)
{
machine_to_jobs.Add(entTasks[j].interval);
}
model.AddNoOverlap(machine_to_jobs);
}
var test = all_tasks.Where(t => t.recipe == "MSHTO05").ToList();
// Makespan objective.
IntVar[] all_ends = new IntVar[all_tasks.Count];
for (int j = 0; j < all_tasks.Count; j++)
{
all_ends[j] = all_tasks[j].end;
}
IntVar makespan = model.NewIntVar(0, horizon, "makespan");
model.AddMaxEquality(makespan, all_ends);
model.Minimize(makespan);
#endregion
// Creates the solver and solve.
CpSolver solver = new CpSolver();
//максимальное время расчёта
solver.StringParameters = "max_time_in_seconds:" + "40";
solver.Solve(model);
#region Преобразование в EntityHistory[] для Gantt chart
List <EntityHistory> solvedEntityHistories = new List <EntityHistory>();
DateTime firstDate = new DateTime(1970, 1, 1, 0, 0, 0);
long startTime = (long)(date.AddMinutes(addMinutes) - firstDate).TotalSeconds;
int id = 0;
foreach (var ent in diffList)
{
var tempEntityHistory = new EntityHistory();
tempEntityHistory.Id = id;
tempEntityHistory.Parent = ent;
tempEntityHistory.Name = ent + "_план";
var entAllTasks = all_tasks.Where(t => t.entity == ent).ToList();
foreach (Task task in entAllTasks)
{
LotInfoPeriodGantt per = new LotInfoPeriodGantt();
per.color = getColor(task.priority);
per.start = (startTime + solver.Value(task.start) * 60) * 1000 + 120000;
per.end = (startTime + solver.Value(task.end) * 60) * 1000 - 120000;
per.duration = task.duration;
per.id = id;
per.lot = task.interval.Name();
per.operation = task.operation.ToString();
per.recipe = task.recipe;
per.connectTo = "";
per.connectorType = "finish - start";
tempEntityHistory.Periods.Add(per);
id++;
}
solvedEntityHistories.Add(tempEntityHistory);
}
#endregion
return(solvedEntityHistories.ToArray());
}
public static void WiTiCp(List <TaskWiti> tasks)
{
var model = new CpModel();
//int variablesMaxValue = 0;
int timeSum = tasks.Sum(x => x.P);
//foreach (var task in tasks)
// variablesMaxValue += ((timeSum - task.D) > 0 ? (timeSum - task.D) : 0) * task.W;
IntVar[] starts = new IntVar[tasks.Count];
IntVar[] ends = new IntVar[tasks.Count];
IntVar[] punishment = new IntVar[tasks.Count];
IntervalVar[] intervals = new IntervalVar[tasks.Count];
//var witi = model.NewIntVar(0, variablesMaxValue, "witi");
for (int i = 0; i < tasks.Count; i++)
{
starts[i] = model.NewIntVar(0, timeSum, $"start_task{i}");
}
for (int i = 0; i < tasks.Count; i++)
{
ends[i] = model.NewIntVar(0, timeSum, $"end_task{i}");
intervals[i] = model.NewIntervalVar(starts[i], tasks[i].P, ends[i], $"interval{i}");
}
for (int i = 0; i < tasks.Count; i++)
{
if ((timeSum - tasks[i].D) > 0)
{
punishment[i] = model.NewIntVar(0, (timeSum - tasks[i].D) * tasks[i].W, $"punish_task{i}");
}
else
{
punishment[i] = model.NewIntVar(0, 0, $"punish_task{i}");
}
}
model.AddNoOverlap(intervals);
/*
* foreach (var task in tasks)
* {
* model.Add(ends[tasks.IndexOf(task)] == starts[tasks.IndexOf(task)] + task.P);
* }
* foreach (var task in tasks)
* {
* model.Add(witi >= (ends[tasks.IndexOf(task)] - task.D) * task.W);
* }*/
foreach (var task in tasks)
{
model.Add(punishment[tasks.IndexOf(task)] >= (ends[tasks.IndexOf(task)] - task.D) * task.W);
}
//foreach (var task in tasks)
//{
// model.Add(punishment[tasks.IndexOf(task)] <= ((timeSum - task.D) > 0 ? (timeSum - task.D) : 0) * task.W);
//}
var witi = punishment.Sum();
model.Minimize(witi);
CpSolver solver = new CpSolver();
solver.StringParameters = "max_time_in_seconds:30.0";
CpSolverStatus solverStatus = solver.Solve(model);
if (solverStatus != CpSolverStatus.Optimal)
{
Console.WriteLine("Solver didn’t find optimal solution!");
}
Console.WriteLine("Objective value = " + solver.Value(witi));
}
static void Main(string[] args)
{
InitTaskList();
int taskCount = GetTaskCount();
Solver solver = new Solver("ResourceConstraintScheduling");
IntervalVar[] tasks = new IntervalVar[taskCount];
IntVar[] taskChoosed = new IntVar[taskCount];
IntVar[] makeSpan = new IntVar[GetEndTaskCount()];
int endJobCounter = 0;
foreach (Job j in myJobList)
{
IntVar[] tmp = new IntVar[j.AlternativeTasks.Count];
int i = 0;
foreach (Task t in j.AlternativeTasks)
{
long ti = taskIndexes[t.Name];
taskChoosed[ti] = solver.MakeIntVar(0, 1, t.Name + "_choose");
tmp[i++] = taskChoosed[ti];
tasks[ti] = solver.MakeFixedDurationIntervalVar(
0, 100000, t.Duration, false, t.Name + "_interval");
if (j.Successor == null)
{
makeSpan[endJobCounter++] = tasks[ti].EndExpr().Var();
}
if (!tasksToEquipment.ContainsKey(t.Equipment))
{
tasksToEquipment[t.Equipment] = new List <IntervalVar>();
}
tasksToEquipment[t.Equipment].Add(tasks[ti]);
}
solver.Add(IntVarArrayHelper.Sum(tmp) == 1);
}
List <SequenceVar> all_seq = new List <SequenceVar>();
foreach (KeyValuePair <long, List <IntervalVar> > pair in tasksToEquipment)
{
DisjunctiveConstraint dc = solver.MakeDisjunctiveConstraint(
pair.Value.ToArray(), pair.Key.ToString());
solver.Add(dc);
all_seq.Add(dc.SequenceVar());
}
IntVar objective_var = solver.MakeMax(makeSpan).Var();
OptimizeVar objective_monitor = solver.MakeMinimize(objective_var, 1);
DecisionBuilder sequence_phase =
solver.MakePhase(all_seq.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder objective_phase =
solver.MakePhase(objective_var, Solver.CHOOSE_FIRST_UNBOUND,
Solver.ASSIGN_MIN_VALUE);
DecisionBuilder main_phase = solver.Compose(sequence_phase, objective_phase);
const int kLogFrequency = 1000000;
VoidToString prefix = new Prefix();
SearchMonitor search_log =
solver.MakeSearchLog(kLogFrequency, objective_monitor, prefix);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(all_seq.ToArray());
collector.AddObjective(objective_var);
if (solver.Solve(main_phase, search_log, objective_monitor, null, collector))
{
Console.Out.WriteLine("Optimal solution = " + collector.ObjectiveValue(0));
}
else
{
Console.Out.WriteLine("No solution.");
}
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(int count,
long start_min,
long start_max,
long duration,
bool optional) {
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i) {
array[i] = MakeFixedDurationIntervalVar(start_min,
start_max,
duration,
optional,
"");
}
return array;
}
static void Main(string[] args)
{
InitTaskList();
int taskCount = GetTaskCount();
Solver solver = new Solver("ResourceConstraintScheduling");
IntervalVar[] tasks = new IntervalVar[taskCount];
IntVar[] taskChoosed = new IntVar[taskCount];
IntVar[] makeSpan = new IntVar[GetEndTaskCount()];
int endJobCounter = 0;
foreach (Job j in myJobList) {
IntVar[] tmp = new IntVar[j.AlternativeTasks.Count];
int i = 0;
foreach (Task t in j.AlternativeTasks) {
long ti = taskIndexes[t.Name];
taskChoosed[ti] = solver.MakeIntVar(0, 1, t.Name + "_choose");
tmp[i++] = taskChoosed[ti];
tasks[ti] = solver.MakeFixedDurationIntervalVar(
0, 100000, t.Duration, false, t.Name + "_interval");
if (j.Successor == null)
makeSpan[endJobCounter++] = tasks[ti].EndExpr().Var();
if (!tasksToEquipment.ContainsKey(t.Equipment))
tasksToEquipment[t.Equipment] = new List<IntervalVar>();
tasksToEquipment[t.Equipment].Add(tasks[ti]);
}
solver.Add(IntVarArrayHelper.Sum(tmp) == 1);
}
List<SequenceVar> all_seq = new List<SequenceVar>();
foreach (KeyValuePair<long, List<IntervalVar>> pair in tasksToEquipment) {
DisjunctiveConstraint dc = solver.MakeDisjunctiveConstraint(
pair.Value.ToArray(), pair.Key.ToString());
solver.Add(dc);
all_seq.Add(dc.SequenceVar());
}
IntVar objective_var = solver.MakeMax(makeSpan).Var();
OptimizeVar objective_monitor = solver.MakeMinimize(objective_var, 1);
DecisionBuilder sequence_phase =
solver.MakePhase(all_seq.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder objective_phase =
solver.MakePhase(objective_var, Solver.CHOOSE_FIRST_UNBOUND,
Solver.ASSIGN_MIN_VALUE);
DecisionBuilder main_phase = solver.Compose(sequence_phase, objective_phase);
const int kLogFrequency = 1000000;
SearchMonitor search_log =
solver.MakeSearchLog(kLogFrequency, objective_monitor);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(all_seq.ToArray());
collector.AddObjective(objective_var);
if (solver.Solve(main_phase, search_log, objective_monitor, null, collector))
Console.Out.WriteLine("Optimal solution = " + collector.ObjectiveValue(0));
else
Console.Out.WriteLine("No solution.");
}
public static void Solve(FlexibleJobShopData data, bool chatty = false)
{
// Compute horizon
var horizon = data.JobList.Sum(
j => j.TaskList.Sum(
t => t.Durations.Max()));
// ----- Create all intervals and vars -----
/*
* // Use some profiling and change the default parameters of the solver
* SolverParameters solverParams = new SolverParameters();
* // Change the profile level
* solverParams.profile_level = SolverParameters.NORMAL_PROFILING;
* // Constraint programming engine
* Solver solver = new Solver("JobShop", solverParams);
*/
// Constraint programming engine
Solver solver = new Solver($"FlexibleJobShop: {data.Name}");
// Initialize dictionaries to hold references to task intervals
var tasksByJobId = new Dictionary <uint, List <TaskAlternative> >();
foreach (var job in data.JobList)
{
tasksByJobId[job.Id] = new List <TaskAlternative>(job.TaskList.Count);
}
var tasksByMachineId = new Dictionary <uint, IntervalVarVector>();
foreach (var machine in data.MachineList)
{
tasksByMachineId[machine.Id] = new IntervalVarVector();
}
// Creates all individual interval variables and collect in dictionaries
foreach (var job in data.JobList)
{
foreach (var task in job.TaskList)
{
var alternative = new TaskAlternative(job.Id);
tasksByJobId[job.Id].Add(alternative);
var activeVariables = new IntVarVector();
var hasAlternatives = task.AlternativesCount > 1;
for (int alt = 0; alt < task.AlternativesCount; alt++)
{
var machine = task.Machines[alt];
var duration = task.Durations[alt];
var name = $"{task.Name}; Alternative {alt}: {machine.Name}, Duration {duration}";
IntervalVar interval = solver.MakeFixedDurationIntervalVar(0, horizon, duration, hasAlternatives, name);
alternative.Add(interval);
tasksByMachineId[machine.Id].Add(interval);
if (hasAlternatives)
{
activeVariables.Add(interval.PerformedExpr().Var());
}
}
alternative.AlternativeVar = solver.MakeIntVar(0, task.AlternativesCount - 1, task.Name);
if (hasAlternatives)
{
solver.Add(solver.MakeMapDomain(alternative.AlternativeVar, activeVariables));
}
}
}
// ----- Create model -----
// Create precedences inside jobs
foreach (var taskAltList in tasksByJobId.Values)
{
for (int i = 0; i < taskAltList.Count - 1; i++)
{
TaskAlternative currentTaskAlt = taskAltList[i];
TaskAlternative nextTaskAlt = taskAltList[i + 1];
foreach (var alt1 in currentTaskAlt)
{
foreach (var alt2 in nextTaskAlt)
{
solver.Add(solver.MakeIntervalVarRelation(
alt2, Solver.STARTS_AFTER_END, alt1));
}
}
}
}
// Collect alternative variables.
IntVarVector alternativeVariableVec = new IntVarVector();
foreach (var taskAltList in tasksByJobId.Values)
{
foreach (var taskAlt in taskAltList)
{
if (!taskAlt.AlternativeVar.Bound())
{
alternativeVariableVec.Add(taskAlt.AlternativeVar);
}
}
}
// Add disjunctive constraints on unary resources, and create
// sequence variables. A sequence variable is a dedicated variable
// whose job is to sequence interval variables.
SequenceVarVector allSequences = new SequenceVarVector();
foreach (var machine in data.MachineList)
{
DisjunctiveConstraint disjCt = solver.MakeDisjunctiveConstraint(
tasksByMachineId[machine.Id], machine.Name);
solver.Add(disjCt);
allSequences.Add(disjCt.SequenceVar());
}
// Create array of end_times of jobs
IntVarVector endsVec = new IntVarVector();
foreach (var taskAltList in tasksByJobId.Values)
{
TaskAlternative lastTaskAlt = taskAltList.Last();
foreach (var alt in lastTaskAlt)
{
endsVec.Add(alt.SafeEndExpr(-1).Var());
}
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(endsVec).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// ----- Search monitors and decision builder -----
// This decision builder will assign all alternative variables.
DecisionBuilder alternativePhase = solver.MakePhase(alternativeVariableVec, Solver.CHOOSE_MIN_SIZE, Solver.ASSIGN_MIN_VALUE);
// This decision builder will rank all tasks on all machines.
DecisionBuilder sequencePhase = solver.MakePhase(allSequences, Solver.SEQUENCE_DEFAULT);
// After the ranking of tasks, the schedule is still loose and any
// task can be postponed at will. But, because the problem is now a PERT
// (http://en.wikipedia.org/wiki/Program_Evaluation_and_Review_Technique),
// we can schedule each task at its earliest start time. This is
// conveniently done by fixing the objective variable to its
// minimum value.
DecisionBuilder objectivePhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objective_variable).
DecisionBuilder mainPhase = solver.Compose(alternativePhase, sequencePhase, objectivePhase);
// Search log
const int kLogFrequency = 1000000;
SearchMonitor searchLog = solver.MakeSearchLog(kLogFrequency, objectiveMonitor);
const long FLAGS_time_limit_in_ms = 1000 * 60 * 20;
SearchLimit limit = null;
if (FLAGS_time_limit_in_ms > 0)
{
limit = solver.MakeTimeLimit(FLAGS_time_limit_in_ms);
}
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.AddObjective(objectiveVar);
collector.Add(alternativeVariableVec);
collector.Add(allSequences);
foreach (var taskVec in tasksByMachineId.Values)
{
foreach (var task in taskVec)
{
collector.Add(task.StartExpr().Var());
}
}
// ----- Search -----
bool solutionFound = solver.Solve(mainPhase, searchLog, objectiveMonitor, limit, collector);
if (solutionFound)
{
// The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
Console.WriteLine();
uint machineIdx = 0;
foreach (var seq in allSequences)
{
machineIdx++;
var taskSeq = collector.ForwardSequence(SOLUTION_INDEX, seq);
Console.WriteLine($"{seq.Name()}:");
Console.WriteLine(" Tasks: " + string.Join(", ", taskSeq));
//foreach (var taskIndex in storedSequence)
//{
// IntervalVar task = sequence.Interval(taskIndex);
// long startMin = solution.StartMin(task);
// long startMax = solution.StartMax(task);
// if (startMin == startMax)
// {
// Console.WriteLine($"Task {task.Name()} starts at {startMin}.");
// }
// else
// {
// Console.WriteLine($"Task {task.Name()} starts between {startMin} and {startMax}.");
// }
//}
Console.WriteLine();
Console.Write(" Starting times:");
foreach (var s in taskSeq)
{
Console.Write(" " + collector.Value(0, tasksByMachineId[machineIdx][s].StartExpr().Var()).ToString());
}
Console.WriteLine();
}
//var x = tasksByMachineId[1][0].StartExpr().Var();
//var xValStr = collector.Value(0, x).ToString();
Console.WriteLine("objective function value = " + solution.ObjectiveValue()); //TEMP
}
// Save profile in file
//solver.ExportProfilingOverview("profile.txt");
// Done
solver.EndSearch();
}
public Task(IntVar s, IntVar e, IntervalVar i)
{
start = s;
end = e;
interval = i;
}
public void RunJobShopScheduling(String solverType)
{
Solver solver = new Solver(solverType);
if (solver == null)
{
Console.WriteLine("JobShop failed to create a solver " + solverType);
return;
}
// All tasks
Dictionary <string, IntervalVar> allTasks = new Dictionary <string, IntervalVar>();
// Creates jobs
for (int i = 0; i < allJobs.Count; i++)
{
for (int j = 0; j < machines.ElementAt(i).Count; j++)
{
IntervalVar oneTask = solver.MakeFixedDurationIntervalVar(0,
horizon,
processingTimes.ElementAt(i).ElementAt(j),
false,
"Job_" + i + "_" + j);
allTasks.Add("Job_" + i + "_" + j, oneTask);
}
}
// Create sequence variables and add disjuctive constraints
List <SequenceVar> allSequences = new List <SequenceVar>();
foreach (var machine in allMachines)
{
List <IntervalVar> machinesJobs = new List <IntervalVar>();
for (int i = 0; i < allJobs.Count; i++)
{
for (int k = 0; k < machines.ElementAt(i).Count; k++)
{
if (machines.ElementAt(i).ElementAt(k) == machine)
{
machinesJobs.Add(allTasks["Job_" + i + "_" + k]);
}
}
}
DisjunctiveConstraint disj = solver.MakeDisjunctiveConstraint(machinesJobs.ToArray(),
"machine " + machine);
allSequences.Add(disj.SequenceVar());
solver.Add(disj);
}
// Add conjunctive constraints
foreach (var job in allJobs)
{
for (int j = 0; j < machines.ElementAt(job).Count - 1; j++)
{
solver.Add(allTasks["Job_" + job + "_" + (j + 1)].StartsAfterEnd(allTasks["Job_" + job + "_" + j]));
}
}
// Set the objective
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < allJobs.Count; i++)
{
allEnds[i] = allTasks["Job_" + i + "_" + (machines.ElementAt(i).Count - 1)].EndExpr().Var();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(allEnds).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// Create search phases
DecisionBuilder sequencePhase = solver.MakePhase(allSequences.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder varsPhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objectiveVariable).
DecisionBuilder mainPhase = solver.Compose(sequencePhase, varsPhase);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(allSequences.ToArray());
collector.Add(objectiveVar);
foreach (var i in allMachines)
{
SequenceVar sequence = allSequences.ElementAt(i);
long sequenceCount = sequence.Size();
for (int j = 0; j < sequenceCount; j++)
{
IntervalVar t = sequence.Interval(j);
collector.Add(t.StartExpr().Var());
collector.Add(t.EndExpr().Var());
}
}
// Search.
bool solutionFound = solver.Solve(mainPhase, null, objectiveMonitor, null, collector);
if (solutionFound)
{
//The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
string solLine = "";
string solLineTasks = "";
Console.WriteLine("Time Intervals for Tasks\n");
for (int m = 0; m < this.machinesCount; m++)
{
//Console.WriteLine("MachineCount: " + this.machinesCount);
solLine = "Machine " + m + " :";
solLineTasks = "Machine " + m + ": ";
SequenceVar seq = allSequences.ElementAt(m);
int[] storedSequence = collector.ForwardSequence(SOLUTION_INDEX, seq);
foreach (int taskIndex in storedSequence)
{
//Console.WriteLine("taskIndex: " + taskIndex);
IntervalVar task = seq.Interval(taskIndex);
solLineTasks += task.Name() + " ";
//Console.WriteLine("Len: " + storedSequence.Length);
}
foreach (int taskIndex in storedSequence)
{
IntervalVar task = seq.Interval(taskIndex);
string solTemp = "[" + collector.Value(0, task.StartExpr().Var()) + ",";
solTemp += collector.Value(0, task.EndExpr().Var()) + "] ";
solLine += solTemp;
}
//solLine += "\n";
solLineTasks += "\n";
//Console.WriteLine(solLineTasks);
Console.WriteLine(solLine);
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public static void Main(String[] args)
{
InitTaskList();
CpModel model = new CpModel();
// ----- Creates all intervals and integer variables -----
// Stores all tasks attached interval variables per job.
List <List <IntervalVar> > jobsToTasks = new List <List <IntervalVar> >(jobsCount);
List <List <IntVar> > jobsToStarts = new List <List <IntVar> >(jobsCount);
List <List <IntVar> > jobsToEnds = new List <List <IntVar> >(jobsCount);
// machinesToTasks stores the same interval variables as above, but
// grouped my machines instead of grouped by jobs.
List <List <IntervalVar> > machinesToTasks = new List <List <IntervalVar> >(machinesCount);
List <List <IntVar> > machinesToStarts = new List <List <IntVar> >(machinesCount);
for (int i = 0; i < machinesCount; i++)
{
machinesToTasks.Add(new List <IntervalVar>());
machinesToStarts.Add(new List <IntVar>());
}
// Creates all individual interval variables.
foreach (List <Task> job in myJobList)
{
jobsToTasks.Add(new List <IntervalVar>());
jobsToStarts.Add(new List <IntVar>());
jobsToEnds.Add(new List <IntVar>());
foreach (Task task in job)
{
IntVar start = model.NewIntVar(0, horizon, task.Name);
IntVar end = model.NewIntVar(0, horizon, task.Name);
IntervalVar oneTask = model.NewIntervalVar(start, task.Duration, end, task.Name);
jobsToTasks[task.JobId].Add(oneTask);
jobsToStarts[task.JobId].Add(start);
jobsToEnds[task.JobId].Add(end);
machinesToTasks[task.Machine].Add(oneTask);
machinesToStarts[task.Machine].Add(start);
}
}
// ----- Creates model -----
// Creates precedences inside jobs.
for (int j = 0; j < jobsToTasks.Count; ++j)
{
for (int t = 0; t < jobsToTasks[j].Count - 1; ++t)
{
model.Add(jobsToEnds[j][t] <= jobsToStarts[j][t + 1]);
}
}
// Adds no_overkap constraints on unary resources.
for (int machineId = 0; machineId < machinesCount; ++machineId)
{
model.AddNoOverlap(machinesToTasks[machineId]);
}
// Creates array of end_times of jobs.
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < jobsCount; i++)
{
allEnds[i] = jobsToEnds[i].Last();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar makespan = model.NewIntVar(0, horizon, "makespan");
model.AddMaxEquality(makespan, allEnds);
model.Minimize(makespan);
// Create the solver.
CpSolver solver = new CpSolver();
// Set the time limit.
if (timeLimitInSeconds > 0)
{
solver.StringParameters = "max_time_in_seconds:" + timeLimitInSeconds;
}
// Solve the problem.
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine("Makespan = " + solver.ObjectiveValue);
for (int m = 0; m < machinesCount; ++m)
{
Console.WriteLine($"Machine {m}:");
SortedDictionary <long, string> starts = new SortedDictionary <long, string>();
foreach (IntVar var in machinesToStarts[m])
{
starts[solver.Value(var)] = var.Name();
}
foreach (KeyValuePair <long, string> p in starts)
{
Console.WriteLine($" Task {p.Value} starts at {p.Key}");
}
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public IntervalVar[] MakeFixedDurationIntervalVarArray(IntVar[] starts,
long[] durations,
string name) {
int count = starts.Length;
IntervalVar[] array = new IntervalVar[count];
for (int i = 0; i < count; ++i) {
array[i] = MakeFixedDurationIntervalVar(starts[i],
durations[i],
name + i);
}
return array;
}
/**
*
* Moving furnitures (scheduling) problem in Google CP Solver.
*
* Marriott & Stukey: 'Programming with constraints', page 112f
*
* Also see http://www.hakank.org/or-tools/furniture_moving.py
*
*/
private static void Solve()
{
Solver solver = new Solver("FurnitureMovingIntervals");
const int n = 4;
int[] durations = { 30, 10, 15, 15 };
int[] demand = { 3, 1, 3, 2 };
const int upper_limit = 160;
const int max_num_workers = 5;
//
// Decision variables
//
IntervalVar[] tasks = new IntervalVar[n];
for (int i = 0; i < n; ++i)
{
tasks[i] =
solver.MakeFixedDurationIntervalVar(0, upper_limit - durations[i], durations[i], false, "task_" + i);
}
// Fillers that span the whole resource and limit the available
// number of workers.
IntervalVar[] fillers = new IntervalVar[max_num_workers];
for (int i = 0; i < max_num_workers; ++i)
{
fillers[i] = solver.MakeFixedDurationIntervalVar(0, 0, upper_limit, true, "filler_" + i);
}
// Number of needed resources, to be minimized or constrained.
IntVar num_workers = solver.MakeIntVar(0, max_num_workers, "num_workers");
// Links fillers and num_workers.
for (int i = 0; i < max_num_workers; ++i)
{
solver.Add((num_workers > i) + fillers[i].PerformedExpr() == 1);
}
// Creates makespan.
IntVar[] ends = new IntVar[n];
for (int i = 0; i < n; ++i)
{
ends[i] = tasks[i].EndExpr().Var();
}
IntVar end_time = ends.Max().VarWithName("end_time");
//
// Constraints
//
IntervalVar[] all_tasks = new IntervalVar[n + max_num_workers];
int[] all_demands = new int[n + max_num_workers];
for (int i = 0; i < n; ++i)
{
all_tasks[i] = tasks[i];
all_demands[i] = demand[i];
}
for (int i = 0; i < max_num_workers; ++i)
{
all_tasks[i + n] = fillers[i];
all_demands[i + n] = 1;
}
solver.Add(all_tasks.Cumulative(all_demands, max_num_workers, "workers"));
//
// Some extra constraints to play with
//
// all tasks must end within an hour
// solver.Add(end_time <= 60);
// All tasks should start at time 0
// for(int i = 0; i < n; i++) {
// solver.Add(tasks[i].StartAt(0));
// }
// limitation of the number of people
// solver.Add(num_workers <= 3);
solver.Add(num_workers <= 4);
//
// Objective
//
// OptimizeVar obj = num_workers.Minimize(1);
OptimizeVar obj = end_time.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(all_tasks, Solver.INTERVAL_DEFAULT);
solver.NewSearch(db, obj);
while (solver.NextSolution())
{
Console.WriteLine(num_workers.ToString() + ", " + end_time.ToString());
for (int i = 0; i < n; i++)
{
Console.WriteLine("{0} (demand:{1})", tasks[i].ToString(), demand[i]);
}
Console.WriteLine();
}
Console.WriteLine("Solutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0} ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}
/**
*
* Moving furnitures (scheduling) problem in Google CP Solver.
*
* Marriott & Stukey: 'Programming with constraints', page 112f
*
* Also see http://www.hakank.org/or-tools/furniture_moving.py
*
*/
private static void Solve()
{
Solver solver = new Solver("FurnitureMovingIntervals");
const int n = 4;
int[] durations = {30,10,15,15};
int[] demand = {3, 1, 3, 2};
const int upper_limit = 160;
const int max_num_workers = 5;
//
// Decision variables
//
IntervalVar[] tasks = new IntervalVar[n];
for (int i = 0; i < n; ++i)
{
tasks[i] = solver.MakeFixedDurationIntervalVar(0,
upper_limit - durations[i],
durations[i],
false,
"task_" + i);
}
// Fillers that span the whole resource and limit the available
// number of workers.
IntervalVar[] fillers = new IntervalVar[max_num_workers];
for (int i = 0; i < max_num_workers; ++i)
{
fillers[i] = solver.MakeFixedDurationIntervalVar(0,
0,
upper_limit,
true,
"filler_" + i);
}
// Number of needed resources, to be minimized or constrained.
IntVar num_workers = solver.MakeIntVar(0, max_num_workers, "num_workers");
// Links fillers and num_workers.
for (int i = 0; i < max_num_workers; ++i)
{
solver.Add((num_workers > i) + fillers[i].PerformedExpr() == 1);
}
// Creates makespan.
IntVar[] ends = new IntVar[n];
for (int i = 0; i < n; ++i)
{
ends[i] = tasks[i].EndExpr().Var();
}
IntVar end_time = ends.Max().VarWithName("end_time");
//
// Constraints
//
IntervalVar[] all_tasks = new IntervalVar[n + max_num_workers];
int[] all_demands = new int[n + max_num_workers];
for (int i = 0; i < n; ++i)
{
all_tasks[i] = tasks[i];
all_demands[i] = demand[i];
}
for (int i = 0; i < max_num_workers; ++i)
{
all_tasks[i + n] = fillers[i];
all_demands[i + n] = 1;
}
solver.Add(all_tasks.Cumulative(all_demands, max_num_workers, "workers"));
//
// Some extra constraints to play with
//
// all tasks must end within an hour
// solver.Add(end_time <= 60);
// All tasks should start at time 0
// for(int i = 0; i < n; i++) {
// solver.Add(tasks[i].StartAt(0));
// }
// limitation of the number of people
// solver.Add(num_workers <= 3);
solver.Add(num_workers <= 4);
//
// Objective
//
// OptimizeVar obj = num_workers.Minimize(1);
OptimizeVar obj = end_time.Minimize(1);
//
// Search
//
DecisionBuilder db = solver.MakePhase(all_tasks, Solver.INTERVAL_DEFAULT);
solver.NewSearch(db, obj);
while (solver.NextSolution()) {
Console.WriteLine(num_workers.ToString() + ", " + end_time.ToString());
for(int i = 0; i < n; i++) {
Console.WriteLine("{0} (demand:{1})", tasks[i].ToString(), demand[i]);
}
Console.WriteLine();
}
Console.WriteLine("Solutions: {0}", solver.Solutions());
Console.WriteLine("WallTime: {0} ms", solver.WallTime());
Console.WriteLine("Failures: {0}", solver.Failures());
Console.WriteLine("Branches: {0} ", solver.Branches());
solver.EndSearch();
}