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minor polish; update c# code in CP-SAT cookbook

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This commit is contained in:
Laurent Perron
2020-11-03 14:01:06 +01:00
parent da09c63cc3
commit 1186dc6196
12 changed files with 772 additions and 717 deletions
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5
ortools/constraint_solver/constraint_solver.h
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@@ -2200,10 +2200,9 @@ class Solver {
/// Creates a search limit that constrains the running time.
RegularLimit* MakeTimeLimit(absl::Duration time);
#if !defined(SWIG)
ABSL_DEPRECATED("Use the version taking absl::Duration() as argument")
#endif
#endif // !defined(SWIG)
RegularLimit* MakeTimeLimit(int64 time_in_ms) {
return MakeTimeLimit(time_in_ms == kint64max
? absl::InfiniteDuration()
@@ -2234,7 +2233,7 @@ class Solver {
#if !defined(SWIG)
ABSL_DEPRECATED("Use other MakeLimit() versions")
#endif
#endif // !defined(SWIG)
RegularLimit* MakeLimit(int64 time, int64 branches, int64 failures,
int64 solutions, bool smart_time_check = false,
bool cumulative = false);

2
ortools/flatzinc/fz.cc
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@@ -55,8 +55,6 @@ ABSL_FLAG(std::string, params, "", "SatParameters as a text proto.");
ABSL_DECLARE_FLAG(bool, log_prefix);
using operations_research::ThreadPool;
namespace operations_research {
namespace fz {

2
ortools/flatzinc/mznlib/BUILD
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@@ -10,6 +10,6 @@ package(default_visibility = ["//visibility:public"])
# Definition files (.mzn).
filegroup(
name = "minizinc_sat_files",
name = "minizinc_files",
srcs = glob(["*.mzn"]),
)

3
ortools/linear_solver/linear_solver.proto
View File

@@ -297,9 +297,6 @@ message MPModelProto {
// To support 'unspecified' double value in proto3, the simplest is to wrap
// any double value in a nested message (has_XXX works for message fields).
// We don't use google/protobuf/wrappers.proto because depending on it makes
// the following android integration test fail:
// http://sponge/c4bce1fd-41bd-4d0b-b4ca-fc04d4d64621
message OptionalDouble {
optional double value = 1;
}

5
ortools/sat/README.md
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@@ -5,7 +5,7 @@ clause learning. It is built on top of an efficient SAT/max-SAT solver whose
code is also in this directory.
To begin, skim
[cp_model.proto](../sat/cp_model.proto) to
[cp_model.proto](../cp_model.proto) to
understand how optimization problems can be modeled using the solver. You can
then solve a model with the functions in
[cp_model_solver.h](../sat/cp_model_solver.h).
@@ -20,7 +20,7 @@ then solve a model with the functions in
The optimization model description and related utilities:
* [cp_model.proto](../sat/cp_model.proto):
* [cp_model.proto](../cp_model.proto):
Proto describing a general Constraint Programming model.
* [cp_model_utils.h](../sat/cp_model_utils.h):
Utilities to manipulate and create a cp_model.proto.
@@ -117,7 +117,6 @@ Scheduling constraints:
Propagation algorithms for the disjunctive scheduling constraint.
* [cumulative.h](../sat/cumulative.h),
[timetable.h](../sat/timetable.h),
[overload_checker.h](../sat/overload_checker.h),
[timetable_edgefinding.h](../sat/timetable_edgefinding.h):
Propagation algorithms for the cumulative scheduling constraint.
* [cumulative_energy.h](../sat/cumulative_energy.h):

54
ortools/sat/doc/README.md
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@@ -14,7 +14,7 @@
* [Java code samples](#java-code-samples)
* [C# code samples](#c-code-samples-1)
<!-- Added by: lperron, at: Thu Nov 14 21:15:53 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:38 CET 2020 -->
<!--te-->
@@ -70,7 +70,7 @@ def SimpleSatProgram():
solver = cp_model.CpSolver()
status = solver.Solve(model)
if status == cp_model.FEASIBLE:
if status == cp_model.OPTIMAL:
print('x = %i' % solver.Value(x))
print('y = %i' % solver.Value(y))
print('z = %i' % solver.Value(z))
@@ -109,7 +109,7 @@ void SimpleSatProgram() {
const CpSolverResponse response = Solve(cp_model.Build());
LOG(INFO) << CpSolverResponseStats(response);
if (response.status() == CpSolverStatus::FEASIBLE) {
if (response.status() == CpSolverStatus::OPTIMAL) {
// Get the value of x in the solution.
LOG(INFO) << "x = " << SolutionIntegerValue(response, x);
LOG(INFO) << "y = " << SolutionIntegerValue(response, y);
@@ -184,31 +184,31 @@ using Google.OrTools.Sat;
public class SimpleSatProgram
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Creates the constraints.
model.Add(x != y);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Feasible)
static void Main()
{
Console.WriteLine("x = " + solver.Value(x));
Console.WriteLine("y = " + solver.Value(y));
Console.WriteLine("z = " + solver.Value(z));
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Creates the constraints.
model.Add(x != y);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine("x = " + solver.Value(x));
Console.WriteLine("y = " + solver.Value(y));
Console.WriteLine("z = " + solver.Value(z));
}
}
}
}
```

68
ortools/sat/doc/boolean_logic.md
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@@ -26,7 +26,7 @@
* [Product of two Boolean Variables](#product-of-two-boolean-variables)
* [Python code](#python-code-3)
<!-- Added by: lperron, at: Thu Nov 14 21:15:53 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:38 CET 2020 -->
<!--te-->
@@ -122,12 +122,14 @@ public class LiteralSampleSat {
using System;
using Google.OrTools.Sat;
public class LiteralSampleSat {
static void Main() {
CpModel model = new CpModel();
IntVar x = model.NewBoolVar("x");
ILiteral not_x = x.Not();
}
public class LiteralSampleSat
{
static void Main()
{
CpModel model = new CpModel();
IntVar x = model.NewBoolVar("x");
ILiteral not_x = x.Not();
}
}
```
@@ -196,6 +198,8 @@ int main() {
### Java code
```java
package com.google.ortools.sat.samples;
import com.google.ortools.sat.CpModel;
import com.google.ortools.sat.IntVar;
import com.google.ortools.sat.Literal;
@@ -220,15 +224,17 @@ public class BoolOrSampleSat {
using System;
using Google.OrTools.Sat;
public class BoolOrSampleSat {
static void Main() {
CpModel model = new CpModel();
public class BoolOrSampleSat
{
static void Main()
{
CpModel model = new CpModel();
IntVar x = model.NewBoolVar("x");
IntVar y = model.NewBoolVar("y");
IntVar x = model.NewBoolVar("x");
IntVar y = model.NewBoolVar("y");
model.AddBoolOr(new ILiteral[] { x, y.Not() });
}
model.AddBoolOr(new ILiteral[] { x, y.Not() });
}
}
```
@@ -324,6 +330,8 @@ int main() {
### Java code
```java
package com.google.ortools.sat.samples;
import com.google.ortools.sat.CpModel;
import com.google.ortools.sat.IntVar;
import com.google.ortools.sat.Literal;
@@ -369,25 +377,27 @@ public class ReifiedSampleSat {
using System;
using Google.OrTools.Sat;
public class ReifiedSampleSat {
static void Main() {
CpModel model = new CpModel();
public class ReifiedSampleSat
{
static void Main()
{
CpModel model = new CpModel();
IntVar x = model.NewBoolVar("x");
IntVar y = model.NewBoolVar("y");
IntVar b = model.NewBoolVar("b");
IntVar x = model.NewBoolVar("x");
IntVar y = model.NewBoolVar("y");
IntVar b = model.NewBoolVar("b");
// First version using a half-reified bool and.
model.AddBoolAnd(new ILiteral[] {x, y.Not()}).OnlyEnforceIf(b);
// First version using a half-reified bool and.
model.AddBoolAnd(new ILiteral[] { x, y.Not() }).OnlyEnforceIf(b);
// Second version using implications.
model.AddImplication(b, x);
model.AddImplication(b, y.Not());
// Second version using implications.
model.AddImplication(b, x);
model.AddImplication(b, y.Not());
// Third version using bool or.
model.AddBoolOr(new ILiteral[] {b.Not(), x});
model.AddBoolOr(new ILiteral[] {b.Not(), y.Not()});
}
// Third version using bool or.
model.AddBoolOr(new ILiteral[] { b.Not(), x });
model.AddBoolOr(new ILiteral[] { b.Not(), y.Not() });
}
}
```

267
ortools/sat/doc/channeling.md
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@@ -17,7 +17,7 @@
* [Java code](#java-code-1)
* [C# code](#c-code-3)
<!-- Added by: lperron, at: Thu Nov 14 21:15:54 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:39 CET 2020 -->
<!--te-->
@@ -254,60 +254,64 @@ using System;
using Google.OrTools.Sat;
using Google.OrTools.Util;
public class VarArraySolutionPrinter : CpSolverSolutionCallback {
public VarArraySolutionPrinter(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback() {
public class VarArraySolutionPrinter : CpSolverSolutionCallback
{
public VarArraySolutionPrinter(IntVar[] variables)
{
foreach (IntVar v in variables_) {
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
variables_ = variables;
}
}
private IntVar[] variables_;
public override void OnSolutionCallback()
{
{
foreach (IntVar v in variables_)
{
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
}
}
private IntVar[] variables_;
}
public class ChannelingSampleSat {
static void Main() {
// Create the CP-SAT model.
CpModel model = new CpModel();
public class ChannelingSampleSat
{
static void Main()
{
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our two primary variables.
IntVar x = model.NewIntVar(0, 10, "x");
IntVar y = model.NewIntVar(0, 10, "y");
// Declare our two primary variables.
IntVar x = model.NewIntVar(0, 10, "x");
IntVar y = model.NewIntVar(0, 10, "y");
// Declare our intermediate boolean variable.
IntVar b = model.NewBoolVar("b");
// Declare our intermediate boolean variable.
IntVar b = model.NewBoolVar("b");
// Implement b == (x >= 5).
model.Add(x >= 5).OnlyEnforceIf(b);
model.Add(x < 5).OnlyEnforceIf(b.Not());
// Implement b == (x >= 5).
model.Add(x >= 5).OnlyEnforceIf(b);
model.Add(x < 5).OnlyEnforceIf(b.Not());
// Create our two half-reified constraints.
// First, b implies (y == 10 - x).
model.Add(y == 10 - x).OnlyEnforceIf(b);
// Second, not(b) implies y == 0.
model.Add(y == 0).OnlyEnforceIf(b.Not());
// Create our two half-reified constraints.
// First, b implies (y == 10 - x).
model.Add(y == 10 - x).OnlyEnforceIf(b);
// Second, not(b) implies y == 0.
model.Add(y == 0).OnlyEnforceIf(b.Not());
// Search for x values in increasing order.
model.AddDecisionStrategy(new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Search for x values in increasing order.
model.AddDecisionStrategy(new IntVar[] { x }, DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] {x, y, b});
solver.SearchAllSolutions(model, cb);
}
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, y, b });
solver.SearchAllSolutions(model, cb);
}
}
```
@@ -504,6 +508,8 @@ int main() {
### Java code
```java
package com.google.ortools.sat.samples;
import com.google.ortools.sat.CpSolverStatus;
import com.google.ortools.sat.CpModel;
import com.google.ortools.sat.CpSolver;
@@ -609,91 +615,106 @@ public class BinPackingProblemSat {
using System;
using Google.OrTools.Sat;
public class BinPackingProblemSat {
static void Main() {
// Data.
int bin_capacity = 100;
int slack_capacity = 20;
int num_bins = 5;
public class BinPackingProblemSat
{
static void Main()
{
// Data.
int bin_capacity = 100;
int slack_capacity = 20;
int num_bins = 5;
int[,] items = new int[,] { { 20, 6 }, { 15, 6 }, { 30, 4 }, { 45, 3 } };
int num_items = items.GetLength(0);
int[,] items = new int[,] { { 20, 6 }, { 15, 6 }, { 30, 4 }, { 45, 3 } };
int num_items = items.GetLength(0);
// Model.
CpModel model = new CpModel();
// Model.
CpModel model = new CpModel();
// Main variables.
IntVar[,] x = new IntVar[num_items, num_bins];
for (int i = 0; i < num_items; ++i) {
int num_copies = items[i, 1];
for (int b = 0; b < num_bins; ++b) {
x[i, b] = model.NewIntVar(0, num_copies, String.Format("x_{0}_{1}", i, b));
}
}
// Load variables.
IntVar[] load = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b) {
load[b] = model.NewIntVar(0, bin_capacity, String.Format("load_{0}", b));
}
// Slack variables.
IntVar[] slacks = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b) {
slacks[b] = model.NewBoolVar(String.Format("slack_{0}", b));
}
// Links load and x.
int[] sizes = new int[num_items];
for (int i = 0; i < num_items; ++i) {
sizes[i] = items[i, 0];
}
for (int b = 0; b < num_bins; ++b) {
IntVar[] tmp = new IntVar[num_items];
for (int i = 0; i < num_items; ++i) {
tmp[i] = x[i, b];
}
model.Add(load[b] == tmp.ScalProd(sizes));
}
// Place all items.
for (int i = 0; i < num_items; ++i) {
IntVar[] tmp = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b) {
tmp[b] = x[i, b];
}
model.Add(LinearExpr.Sum(tmp) == items[i, 1]);
}
// Links load and slack.
int safe_capacity = bin_capacity - slack_capacity;
for (int b = 0; b < num_bins; ++b) {
// slack[b] => load[b] <= safe_capacity.
model.Add(load[b] <= safe_capacity).OnlyEnforceIf(slacks[b]);
// not(slack[b]) => load[b] > safe_capacity.
model.Add(load[b] > safe_capacity).OnlyEnforceIf(slacks[b].Not());
}
// Maximize sum of slacks.
model.Maximize(LinearExpr.Sum(slacks));
// Solves and prints out the solution.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
Console.WriteLine(String.Format("Solve status: {0}", status));
if (status == CpSolverStatus.Optimal) {
Console.WriteLine(String.Format("Optimal objective value: {0}", solver.ObjectiveValue));
for (int b = 0; b < num_bins; ++b) {
Console.WriteLine(String.Format("load_{0} = {1}", b, solver.Value(load[b])));
for (int i = 0; i < num_items; ++i) {
Console.WriteLine(string.Format(" item_{0}_{1} = {2}", i, b, solver.Value(x[i, b])));
// Main variables.
IntVar[,] x = new IntVar[num_items, num_bins];
for (int i = 0; i < num_items; ++i)
{
int num_copies = items[i, 1];
for (int b = 0; b < num_bins; ++b)
{
x[i, b] = model.NewIntVar(0, num_copies, String.Format("x_{0}_{1}", i, b));
}
}
}
// Load variables.
IntVar[] load = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b)
{
load[b] = model.NewIntVar(0, bin_capacity, String.Format("load_{0}", b));
}
// Slack variables.
IntVar[] slacks = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b)
{
slacks[b] = model.NewBoolVar(String.Format("slack_{0}", b));
}
// Links load and x.
int[] sizes = new int[num_items];
for (int i = 0; i < num_items; ++i)
{
sizes[i] = items[i, 0];
}
for (int b = 0; b < num_bins; ++b)
{
IntVar[] tmp = new IntVar[num_items];
for (int i = 0; i < num_items; ++i)
{
tmp[i] = x[i, b];
}
model.Add(load[b] == LinearExpr.ScalProd(tmp, sizes));
}
// Place all items.
for (int i = 0; i < num_items; ++i)
{
IntVar[] tmp = new IntVar[num_bins];
for (int b = 0; b < num_bins; ++b)
{
tmp[b] = x[i, b];
}
model.Add(LinearExpr.Sum(tmp) == items[i, 1]);
}
// Links load and slack.
int safe_capacity = bin_capacity - slack_capacity;
for (int b = 0; b < num_bins; ++b)
{
// slack[b] => load[b] <= safe_capacity.
model.Add(load[b] <= safe_capacity).OnlyEnforceIf(slacks[b]);
// not(slack[b]) => load[b] > safe_capacity.
model.Add(load[b] > safe_capacity).OnlyEnforceIf(slacks[b].Not());
}
// Maximize sum of slacks.
model.Maximize(LinearExpr.Sum(slacks));
// Solves and prints out the solution.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
Console.WriteLine(String.Format("Solve status: {0}", status));
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine(String.Format("Optimal objective value: {0}", solver.ObjectiveValue));
for (int b = 0; b < num_bins; ++b)
{
Console.WriteLine(String.Format("load_{0} = {1}", b, solver.Value(load[b])));
for (int i = 0; i < num_items; ++i)
{
Console.WriteLine(string.Format(" item_{0}_{1} = {2}", i, b, solver.Value(x[i, b])));
}
}
}
Console.WriteLine("Statistics");
Console.WriteLine(String.Format(" - conflicts : {0}", solver.NumConflicts()));
Console.WriteLine(String.Format(" - branches : {0}", solver.NumBranches()));
Console.WriteLine(String.Format(" - wall time : {0} s", solver.WallTime()));
}
Console.WriteLine("Statistics");
Console.WriteLine(String.Format(" - conflicts : {0}", solver.NumConflicts()));
Console.WriteLine(String.Format(" - branches : {0}", solver.NumBranches()));
Console.WriteLine(String.Format(" - wall time : {0} s", solver.WallTime()));
}
}
```

282
ortools/sat/doc/integer_arithmetic.md
View File

@@ -33,7 +33,7 @@
* [Java code](#java-code-2)
* [C# code](#c-code-5)
<!-- Added by: lperron, at: Thu Nov 14 21:15:55 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:40 CET 2020 -->
<!--te-->
@@ -172,7 +172,7 @@ def RabbitsAndPheasantsSat():
solver = cp_model.CpSolver()
status = solver.Solve(model)
if status == cp_model.FEASIBLE:
if status == cp_model.OPTIMAL:
print('%i rabbits and %i pheasants' % (solver.Value(r), solver.Value(p)))
@@ -200,7 +200,7 @@ void RabbitsAndPheasantsSat() {
const CpSolverResponse response = Solve(cp_model.Build());
if (response.status() == CpSolverStatus::FEASIBLE) {
if (response.status() == CpSolverStatus::OPTIMAL) {
// Get the value of x in the solution.
LOG(INFO) << SolutionIntegerValue(response, rabbits) << " rabbits, and "
<< SolutionIntegerValue(response, pheasants) << " pheasants";
@@ -263,28 +263,29 @@ public class RabbitsAndPheasantsSat {
using System;
using Google.OrTools.Sat;
public class RabbitsAndPheasantsSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
IntVar r = model.NewIntVar(0, 100, "r");
IntVar p = model.NewIntVar(0, 100, "p");
// 20 heads.
model.Add(r + p == 20);
// 56 legs.
model.Add(4 * r + 2 * p == 56);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Feasible)
public class RabbitsAndPheasantsSat
{
static void Main()
{
Console.WriteLine(solver.Value(r) + " rabbits, and " +
solver.Value(p) + " pheasants");
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
IntVar r = model.NewIntVar(0, 100, "r");
IntVar p = model.NewIntVar(0, 100, "p");
// 20 heads.
model.Add(r + p == 20);
// 56 legs.
model.Add(4 * r + 2 * p == 56);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine(solver.Value(r) + " rabbits, and " + solver.Value(p) + " pheasants");
}
}
}
}
```
@@ -579,76 +580,79 @@ using System;
using Google.OrTools.Sat;
using Google.OrTools.Util;
public class VarArraySolutionPrinter : CpSolverSolutionCallback {
public VarArraySolutionPrinter(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback() {
public class VarArraySolutionPrinter : CpSolverSolutionCallback
{
public VarArraySolutionPrinter(IntVar[] variables)
{
foreach (IntVar v in variables_) {
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
variables_ = variables;
}
}
private IntVar[] variables_;
public override void OnSolutionCallback()
{
{
foreach (IntVar v in variables_)
{
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
}
}
private IntVar[] variables_;
}
public class EarlinessTardinessCostSampleSat {
static void Main() {
long earliness_date = 5;
long earliness_cost = 8;
long lateness_date = 15;
long lateness_cost = 12;
public class EarlinessTardinessCostSampleSat
{
static void Main()
{
long earliness_date = 5;
long earliness_cost = 8;
long lateness_date = 15;
long lateness_cost = 12;
// Create the CP-SAT model.
CpModel model = new CpModel();
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Create the expression variable and implement the piecewise linear
// function.
//
// \ /
// \______/
// ed ld
//
long large_constant = 1000;
IntVar expr = model.NewIntVar(0, large_constant, "expr");
// Create the expression variable and implement the piecewise linear
// function.
//
// \ /
// \______/
// ed ld
//
long large_constant = 1000;
IntVar expr = model.NewIntVar(0, large_constant, "expr");
// First segment.
IntVar s1 = model.NewIntVar(-large_constant, large_constant, "s1");
model.Add(s1 == earliness_cost * (earliness_date - x));
// First segment.
IntVar s1 = model.NewIntVar(-large_constant, large_constant, "s1");
model.Add(s1 == earliness_cost * (earliness_date - x));
// Second segment.
IntVar s2 = model.NewConstant(0);
// Second segment.
IntVar s2 = model.NewConstant(0);
// Third segment.
IntVar s3 = model.NewIntVar(-large_constant, large_constant, "s3");
model.Add(s3 == lateness_cost * (x - lateness_date));
// Third segment.
IntVar s3 = model.NewIntVar(-large_constant, large_constant, "s3");
model.Add(s3 == lateness_cost * (x - lateness_date));
// Link together expr and x through s1, s2, and s3.
model.AddMaxEquality(expr, new IntVar[] {s1, s2, s3});
// Link together expr and x through s1, s2, and s3.
model.AddMaxEquality(expr, new IntVar[] { s1, s2, s3 });
// Search for x values in increasing order.
model.AddDecisionStrategy(
new IntVar[] {x},
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Search for x values in increasing order.
model.AddDecisionStrategy(new IntVar[] { x }, DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] {x, expr});
solver.SearchAllSolutions(model, cb);
}
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, expr });
solver.SearchAllSolutions(model, cb);
}
}
```
@@ -947,82 +951,82 @@ using System;
using Google.OrTools.Sat;
using Google.OrTools.Util;
public class VarArraySolutionPrinter : CpSolverSolutionCallback {
public VarArraySolutionPrinter(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback() {
public class VarArraySolutionPrinter : CpSolverSolutionCallback
{
public VarArraySolutionPrinter(IntVar[] variables)
{
foreach (IntVar v in variables_) {
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
variables_ = variables;
}
}
private IntVar[] variables_;
public override void OnSolutionCallback()
{
{
foreach (IntVar v in variables_)
{
Console.Write(String.Format("{0}={1} ", v.ShortString(), Value(v)));
}
Console.WriteLine();
}
}
private IntVar[] variables_;
}
public class StepFunctionSampleSat {
static void Main() {
// Create the CP-SAT model.
CpModel model = new CpModel();
public class StepFunctionSampleSat
{
static void Main()
{
// Create the CP-SAT model.
CpModel model = new CpModel();
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Declare our primary variable.
IntVar x = model.NewIntVar(0, 20, "x");
// Create the expression variable and implement the step function
// Note it is not defined for var == 2.
//
// - 3
// -- -- --------- 2
// 1
// -- --- 0
// 0 ================ 20
//
IntVar expr = model.NewIntVar(0, 3, "expr");
// Create the expression variable and implement the step function
// Note it is not defined for var == 2.
//
// - 3
// -- -- --------- 2
// 1
// -- --- 0
// 0 ================ 20
//
IntVar expr = model.NewIntVar(0, 3, "expr");
// expr == 0 on [5, 6] U [8, 10]
ILiteral b0 = model.NewBoolVar("b0");
model.AddLinearExpressionInDomain(
x,
Domain.FromValues(new long[] { 5, 6, 8, 9, 10 })).OnlyEnforceIf(b0);
model.Add(expr == 0).OnlyEnforceIf(b0);
// expr == 0 on [5, 6] U [8, 10]
ILiteral b0 = model.NewBoolVar("b0");
model.AddLinearExpressionInDomain(x, Domain.FromValues(new long[] { 5, 6, 8, 9, 10 })).OnlyEnforceIf(b0);
model.Add(expr == 0).OnlyEnforceIf(b0);
// expr == 2 on [0, 1] U [3, 4] U [11, 20]
ILiteral b2 = model.NewBoolVar("b2");
model.AddLinearExpressionInDomain(
x,
Domain.FromIntervals(
new long[][] {new long[] {0, 1},
new long[] {3, 4},
new long[] {11, 20}})).OnlyEnforceIf(b2);
model.Add(expr == 2).OnlyEnforceIf(b2);
// expr == 2 on [0, 1] U [3, 4] U [11, 20]
ILiteral b2 = model.NewBoolVar("b2");
model
.AddLinearExpressionInDomain(
x,
Domain.FromIntervals(new long[][] { new long[] { 0, 1 }, new long[] { 3, 4 }, new long[] { 11, 20 } }))
.OnlyEnforceIf(b2);
model.Add(expr == 2).OnlyEnforceIf(b2);
// expr == 3 when x == 7
ILiteral b3 = model.NewBoolVar("b3");
model.Add(x == 7).OnlyEnforceIf(b3);
model.Add(expr == 3).OnlyEnforceIf(b3);
// expr == 3 when x == 7
ILiteral b3 = model.NewBoolVar("b3");
model.Add(x == 7).OnlyEnforceIf(b3);
model.Add(expr == 3).OnlyEnforceIf(b3);
// At least one bi is true. (we could use a sum == 1).
model.AddBoolOr(new ILiteral[] { b0, b2, b3 });
// At least one bi is true. (we could use a sum == 1).
model.AddBoolOr(new ILiteral[] { b0, b2, b3 });
// Search for x values in increasing order.
model.AddDecisionStrategy(
new IntVar[] { x },
DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Search for x values in increasing order.
model.AddDecisionStrategy(new IntVar[] { x }, DecisionStrategyProto.Types.VariableSelectionStrategy.ChooseFirst,
DecisionStrategyProto.Types.DomainReductionStrategy.SelectMinValue);
// Create the solver.
CpSolver solver = new CpSolver();
// Create the solver.
CpSolver solver = new CpSolver();
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
// Force solver to follow the decision strategy exactly.
solver.StringParameters = "search_branching:FIXED_SEARCH";
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, expr });
solver.SearchAllSolutions(model, cb);
}
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, expr });
solver.SearchAllSolutions(model, cb);
}
}
```

85
ortools/sat/doc/model.md
View File

@@ -13,7 +13,7 @@
* [Java code](#java-code)
* [C# code](#c-code-1)
<!-- Added by: lperron, at: Thu Nov 14 21:15:56 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:40 CET 2020 -->
<!--te-->
@@ -246,56 +246,61 @@ public class SolutionHintingSampleSat {
using System;
using Google.OrTools.Sat;
public class VarArraySolutionPrinter : CpSolverSolutionCallback {
public VarArraySolutionPrinter(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback() {
public class VarArraySolutionPrinter : CpSolverSolutionCallback
{
public VarArraySolutionPrinter(IntVar[] variables)
{
Console.WriteLine(
String.Format("Solution #{0}: time = {1:F2} s", solution_count_, WallTime()));
foreach (IntVar v in variables_) {
Console.WriteLine(String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
variables_ = variables;
}
}
public int SolutionCount() {
return solution_count_;
}
public override void OnSolutionCallback()
{
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s", solution_count_, WallTime()));
foreach (IntVar v in variables_)
{
Console.WriteLine(String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
}
}
private int solution_count_;
private IntVar[] variables_;
public int SolutionCount()
{
return solution_count_;
}
private int solution_count_;
private IntVar[] variables_;
}
public class SolutionHintingSampleSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
public class SolutionHintingSampleSat
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Creates the constraints.
model.Add(x != y);
// Creates the constraints.
model.Add(x != y);
// Solution hinting: x <- 1, y <- 2
model.AddHint(x, 1);
model.AddHint(y, 2);
// Solution hinting: x <- 1, y <- 2
model.AddHint(x, 1);
model.AddHint(y, 2);
model.Maximize(LinearExpr.ScalProd(new IntVar[] {x, y, z}, new int[] {1, 2, 3}));
model.Maximize(LinearExpr.ScalProd(new IntVar[] { x, y, z }, new int[] { 1, 2, 3 }));
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, z });
CpSolverStatus status = solver.SolveWithSolutionCallback(model, cb);
}
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, y, z });
CpSolverStatus status = solver.SolveWithSolutionCallback(model, cb);
}
}
```

416
ortools/sat/doc/scheduling.md
View File

@@ -36,7 +36,7 @@
* [Convex hull of a set of intervals](#convex-hull-of-a-set-of-intervals)
* [Reservoir constraint](#reservoir-constraint)
<!-- Added by: lperron, at: Thu Nov 14 21:15:57 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:41 CET 2020 -->
<!--te-->
@@ -156,17 +156,18 @@ public class IntervalSampleSat {
using System;
using Google.OrTools.Sat;
public class IntervalSampleSat {
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 class IntervalSampleSat
{
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");
}
}
```
@@ -284,18 +285,19 @@ public class OptionalIntervalSampleSat {
using System;
using Google.OrTools.Sat;
public class OptionalIntervalSampleSat {
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");
IntVar presence_var = model.NewBoolVar("presence");
IntervalVar interval = model.NewOptionalIntervalVar(
start_var, duration, end_var, presence_var, "interval");
}
public class OptionalIntervalSampleSat
{
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");
IntVar presence_var = model.NewBoolVar("presence");
IntervalVar interval = model.NewOptionalIntervalVar(start_var, duration, end_var, presence_var, "interval");
}
}
```
@@ -529,58 +531,57 @@ public class NoOverlapSampleSat {
using System;
using Google.OrTools.Sat;
public class NoOverlapSampleSat {
static void Main() {
CpModel model = new CpModel();
// Three weeks.
int horizon = 21;
public class NoOverlapSampleSat
{
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 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 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");
// 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");
// 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});
// 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);
// 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);
// 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));
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));
}
}
}
}
```
@@ -1079,144 +1080,159 @@ using System;
using System.Collections.Generic;
using Google.OrTools.Sat;
public class RankingSampleSat {
static void RankTasks(CpModel model,
IntVar[] starts,
ILiteral[] presences,
IntVar[] ranks) {
int num_tasks = starts.Length;
public class RankingSampleSat
{
static void RankTasks(CpModel model, IntVar[] starts, ILiteral[] presences, IntVar[] ranks)
{
int num_tasks = starts.Length;
// Creates precedence variables between pairs of intervals.
ILiteral[,] precedences = new ILiteral[num_tasks, num_tasks];
for (int i = 0; i < num_tasks; ++i) {
for (int j = 0; j < num_tasks; ++j) {
if (i == j) {
precedences[i, i] = presences[i];
} else {
IntVar prec = model.NewBoolVar(String.Format("{0} before {1}", i, j));
precedences[i, j] = prec;
model.Add(starts[i] < starts[j]).OnlyEnforceIf(prec);
// Creates precedence variables between pairs of intervals.
ILiteral[,] precedences = new ILiteral[num_tasks, num_tasks];
for (int i = 0; i < num_tasks; ++i)
{
for (int j = 0; j < num_tasks; ++j)
{
if (i == j)
{
precedences[i, i] = presences[i];
}
else
{
IntVar prec = model.NewBoolVar(String.Format("{0} before {1}", i, j));
precedences[i, j] = prec;
model.Add(starts[i] < starts[j]).OnlyEnforceIf(prec);
}
}
}
}
}
// Treats optional intervals.
for (int i = 0; i < num_tasks - 1; ++i) {
for (int j = i + 1; j < num_tasks; ++j) {
List<ILiteral> tmp_array = new List<ILiteral>();
tmp_array.Add(precedences[i, j]);
tmp_array.Add(precedences[j, i]);
tmp_array.Add(presences[i].Not());
// Makes sure that if i is not performed, all precedences are false.
model.AddImplication(presences[i].Not(), precedences[i, j].Not());
model.AddImplication(presences[i].Not(), precedences[j, i].Not());
tmp_array.Add(presences[j].Not());
// Makes sure that if j is not performed, all precedences are false.
model.AddImplication(presences[j].Not(), precedences[i, j].Not());
model.AddImplication(presences[j].Not(), precedences[j, i].Not());
// The following bool_or will enforce that for any two intervals:
// i precedes j or j precedes i or at least one interval is not
// performed.
model.AddBoolOr(tmp_array);
// Redundant constraint: it propagates early that at most one precedence
// is true.
model.AddImplication(precedences[i, j], precedences[j, i].Not());
model.AddImplication(precedences[j, i], precedences[i, j].Not());
}
}
// Links precedences and ranks.
for (int i = 0; i < num_tasks; ++i) {
IntVar[] tmp_array = new IntVar[num_tasks];
for (int j = 0; j < num_tasks; ++j) {
tmp_array[j] = (IntVar)precedences[j, i];
}
model.Add(ranks[i] == LinearExpr.Sum(tmp_array) - 1);
}
}
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 * LinearExpr.Sum(presences_as_int_vars));
// 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])));
// Treats optional intervals.
for (int i = 0; i < num_tasks - 1; ++i)
{
for (int j = i + 1; j < num_tasks; ++j)
{
List<ILiteral> tmp_array = new List<ILiteral>();
tmp_array.Add(precedences[i, j]);
tmp_array.Add(precedences[j, i]);
tmp_array.Add(presences[i].Not());
// Makes sure that if i is not performed, all precedences are false.
model.AddImplication(presences[i].Not(), precedences[i, j].Not());
model.AddImplication(presences[i].Not(), precedences[j, i].Not());
tmp_array.Add(presences[j].Not());
// Makes sure that if j is not performed, all precedences are false.
model.AddImplication(presences[j].Not(), precedences[i, j].Not());
model.AddImplication(presences[j].Not(), precedences[j, i].Not());
// The following bool_or will enforce that for any two intervals:
// i precedes j or j precedes i or at least one interval is not
// performed.
model.AddBoolOr(tmp_array);
// Redundant constraint: it propagates early that at most one precedence
// is true.
model.AddImplication(precedences[i, j], precedences[j, i].Not());
model.AddImplication(precedences[j, i], precedences[i, j].Not());
}
}
// Links precedences and ranks.
for (int i = 0; i < num_tasks; ++i)
{
IntVar[] tmp_array = new IntVar[num_tasks];
for (int j = 0; j < num_tasks; ++j)
{
tmp_array[j] = (IntVar)precedences[j, i];
}
model.Add(ranks[i] == LinearExpr.Sum(tmp_array) - 1);
}
}
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 * LinearExpr.Sum(presences_as_int_vars));
// 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));
}
}
} else {
Console.WriteLine(
String.Format("Solver exited with nonoptimal status: {0}", status));
}
}
}
```

300
ortools/sat/doc/solver.md
View File

@@ -27,7 +27,7 @@
* [Java code](#java-code-2)
* [C# code](#c-code-5)
<!-- Added by: lperron, at: Thu Nov 14 21:15:58 CET 2019 -->
<!-- Added by: lperron, at: Tue Nov 3 13:54:42 CET 2020 -->
<!--te-->
@@ -69,7 +69,7 @@ def SolveWithTimeLimitSampleSat():
status = solver.Solve(model)
if status == cp_model.FEASIBLE:
if status == cp_model.OPTIMAL:
print('x = %i' % solver.Value(x))
print('y = %i' % solver.Value(y))
print('z = %i' % solver.Value(z))
@@ -110,7 +110,7 @@ void SolveWithTimeLimitSampleSat() {
const CpSolverResponse response = SolveCpModel(cp_model.Build(), &model);
LOG(INFO) << CpSolverResponseStats(response);
if (response.status() == CpSolverStatus::FEASIBLE) {
if (response.status() == CpSolverStatus::OPTIMAL) {
LOG(INFO) << " x = " << SolutionIntegerValue(response, x);
LOG(INFO) << " y = " << SolutionIntegerValue(response, y);
LOG(INFO) << " z = " << SolutionIntegerValue(response, z);
@@ -176,33 +176,36 @@ Parameters must be passed as string to the solver.
using System;
using Google.OrTools.Sat;
public class SolveWithTimeLimitSampleSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
public class SolveWithTimeLimitSampleSat
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Adds a different constraint.
model.Add(x != y);
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Adds a different constraint.
model.Add(x != y);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
// Adds a time limit. Parameters are stored as strings in the solver.
solver.StringParameters = "max_time_in_seconds:10.0";
// Adds a time limit. Parameters are stored as strings in the solver.
solver.StringParameters = "max_time_in_seconds:10.0";
CpSolverStatus status = solver.Solve(model);
CpSolverStatus status = solver.Solve(model);
if (status == CpSolverStatus.Optimal) {
Console.WriteLine("x = " + solver.Value(x));
Console.WriteLine("y = " + solver.Value(y));
Console.WriteLine("z = " + solver.Value(z));
if (status == CpSolverStatus.Optimal)
{
Console.WriteLine("x = " + solver.Value(x));
Console.WriteLine("y = " + solver.Value(y));
Console.WriteLine("z = " + solver.Value(z));
}
}
}
}
```
@@ -394,60 +397,60 @@ public class SolveAndPrintIntermediateSolutionsSampleSat {
using System;
using Google.OrTools.Sat;
public class VarArraySolutionPrinterWithObjective : CpSolverSolutionCallback {
public VarArraySolutionPrinterWithObjective(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback()
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s",
solution_count_, WallTime()));
Console.WriteLine(
String.Format(" objective value = {0}", ObjectiveValue()));
foreach (IntVar v in variables_)
public class VarArraySolutionPrinterWithObjective : CpSolverSolutionCallback
{
public VarArraySolutionPrinterWithObjective(IntVar[] variables)
{
Console.WriteLine(
String.Format(" {0} = {1}", v.ShortString(), Value(v)));
variables_ = variables;
}
solution_count_++;
}
public int SolutionCount() {
return solution_count_;
}
public override void OnSolutionCallback()
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s", solution_count_, WallTime()));
Console.WriteLine(String.Format(" objective value = {0}", ObjectiveValue()));
foreach (IntVar v in variables_)
{
Console.WriteLine(String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
}
private int solution_count_;
private IntVar[] variables_;
public int SolutionCount()
{
return solution_count_;
}
private int solution_count_;
private IntVar[] variables_;
}
public class SolveAndPrintIntermediateSolutionsSampleSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
public class SolveAndPrintIntermediateSolutionsSampleSat
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Adds a different constraint.
model.Add(x != y);
// Adds a different constraint.
model.Add(x != y);
// Maximizes a linear combination of variables.
model.Maximize(x + 2 * y + 3 * z);
// Maximizes a linear combination of variables.
model.Maximize(x + 2 * y + 3 * z);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinterWithObjective cb =
new VarArraySolutionPrinterWithObjective(new IntVar[] { x, y, z });
solver.SolveWithSolutionCallback(model, cb);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinterWithObjective cb = new VarArraySolutionPrinterWithObjective(new IntVar[] { x, y, z });
solver.SolveWithSolutionCallback(model, cb);
Console.WriteLine(String.Format("Number of solutions found: {0}",
cb.SolutionCount()));
}
Console.WriteLine(String.Format("Number of solutions found: {0}", cb.SolutionCount()));
}
}
```
@@ -640,56 +643,58 @@ As in Python, CpSolver.SearchAllSolutions() must be called.
using System;
using Google.OrTools.Sat;
public class VarArraySolutionPrinter : CpSolverSolutionCallback {
public VarArraySolutionPrinter(IntVar[] variables) {
variables_ = variables;
}
public override void OnSolutionCallback() {
public class VarArraySolutionPrinter : CpSolverSolutionCallback
{
public VarArraySolutionPrinter(IntVar[] variables)
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s",
solution_count_, WallTime()));
foreach (IntVar v in variables_)
{
Console.WriteLine(
String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
variables_ = variables;
}
}
public int SolutionCount() {
return solution_count_;
}
public override void OnSolutionCallback()
{
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s", solution_count_, WallTime()));
foreach (IntVar v in variables_)
{
Console.WriteLine(String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
}
}
private int solution_count_;
private IntVar[] variables_;
public int SolutionCount()
{
return solution_count_;
}
private int solution_count_;
private IntVar[] variables_;
}
public class SearchForAllSolutionsSampleSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
public class SearchForAllSolutionsSampleSat
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Adds a different constraint.
model.Add(x != y);
// Adds a different constraint.
model.Add(x != y);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb =
new VarArraySolutionPrinter(new IntVar[] { x, y, z });
solver.SearchAllSolutions(model, cb);
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinter cb = new VarArraySolutionPrinter(new IntVar[] { x, y, z });
solver.SearchAllSolutions(model, cb);
Console.WriteLine(String.Format("Number of solutions found: {0}",
cb.SolutionCount()));
}
Console.WriteLine(String.Format("Number of solutions found: {0}", cb.SolutionCount()));
}
}
```
@@ -902,56 +907,57 @@ CpSolverSolutionCallback.OnSolutionCallback().
using System;
using Google.OrTools.Sat;
public class VarArraySolutionPrinterWithLimit : CpSolverSolutionCallback {
public VarArraySolutionPrinterWithLimit(IntVar[] variables, int solution_limit) {
variables_ = variables;
solution_limit_ = solution_limit;
}
public override void OnSolutionCallback() {
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s",
solution_count_, WallTime()));
foreach (IntVar v in variables_)
public class VarArraySolutionPrinterWithLimit : CpSolverSolutionCallback
{
public VarArraySolutionPrinterWithLimit(IntVar[] variables, int solution_limit)
{
Console.WriteLine(
String.Format(" {0} = {1}", v.ShortString(), Value(v)));
variables_ = variables;
solution_limit_ = solution_limit;
}
solution_count_++;
if (solution_count_ >= solution_limit_) {
Console.WriteLine(
String.Format("Stopping search after {0} solutions",
solution_limit_));
StopSearch();
public override void OnSolutionCallback()
{
Console.WriteLine(String.Format("Solution #{0}: time = {1:F2} s", solution_count_, WallTime()));
foreach (IntVar v in variables_)
{
Console.WriteLine(String.Format(" {0} = {1}", v.ShortString(), Value(v)));
}
solution_count_++;
if (solution_count_ >= solution_limit_)
{
Console.WriteLine(String.Format("Stopping search after {0} solutions", solution_limit_));
StopSearch();
}
}
}
public int SolutionCount() {
return solution_count_;
}
public int SolutionCount()
{
return solution_count_;
}
private int solution_count_;
private IntVar[] variables_;
private int solution_limit_;
private int solution_count_;
private IntVar[] variables_;
private int solution_limit_;
}
public class StopAfterNSolutionsSampleSat {
static void Main() {
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
public class StopAfterNSolutionsSampleSat
{
static void Main()
{
// Creates the model.
CpModel model = new CpModel();
// Creates the variables.
int num_vals = 3;
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
IntVar x = model.NewIntVar(0, num_vals - 1, "x");
IntVar y = model.NewIntVar(0, num_vals - 1, "y");
IntVar z = model.NewIntVar(0, num_vals - 1, "z");
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinterWithLimit cb =
new VarArraySolutionPrinterWithLimit(new IntVar[] { x, y, z }, 5);
solver.SearchAllSolutions(model, cb);
Console.WriteLine(String.Format("Number of solutions found: {0}",
cb.SolutionCount()));
}
// Creates a solver and solves the model.
CpSolver solver = new CpSolver();
VarArraySolutionPrinterWithLimit cb = new VarArraySolutionPrinterWithLimit(new IntVar[] { x, y, z }, 5);
solver.SearchAllSolutions(model, cb);
Console.WriteLine(String.Format("Number of solutions found: {0}", cb.SolutionCount()));
}
}
```