# Solving a Multiple Knapsacks Problem

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This section shows how to solve the knapsack problem for multiple knapsacks using both the MIP solver and the CP-SAT solver. In this case, it's common to refer to the containers as bins, rather than knapsacks.

The next example shows how to find the optimal way to pack items into five bins.

## Example

As in the previous example, you start with a collection of items of varying weights and values. The problem is to pack a subset of the items into five bins, each of which has a maximum capacity of 100, so that the total packed value is a maximum.

The following sections present sections of programs that solve this problem. For the full programs, see Complete programs.

## MIP solution

The following sections describe how to solve the problem using the MPSolver wrapper.

### Import the libraries

The following code imports the required libraries.

### Python

`from ortools.linear_solver import pywraplp`

### C++

```#include <iostream>
#include <memory>
#include <numeric>
#include <vector>

#include "absl/strings/str_format.h"
#include "ortools/linear_solver/linear_expr.h"
#include "ortools/linear_solver/linear_solver.h"```

### Java

```import com.google.ortools.Loader;
import java.util.stream.IntStream;```

### C#

```using System;
using System.Collections.Generic;
using System.Linq;

### Create the data

The following code creates the data for the problem.

### Python

```data = {}
data['weights'] = [
48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36
]
data['values'] = [
10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25
]
assert len(data['weights']) == len(data['values'])
data['num_items'] = len(data['weights'])
data['all_items'] = range(data['num_items'])

data['bin_capacities'] = [100, 100, 100, 100, 100]
data['num_bins'] = len(data['bin_capacities'])
data['all_bins'] = range(data['num_bins'])```

### C++

```const std::vector<int> weights = {
{48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36}};
const std::vector<int> values = {
{10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25}};
const int num_items = weights.size();
std::vector<int> all_items(num_items);
std::iota(all_items.begin(), all_items.end(), 0);

const std::vector<int> bin_capacities = {{100, 100, 100, 100, 100}};
const int num_bins = bin_capacities.size();
std::vector<int> all_bins(num_bins);
std::iota(all_bins.begin(), all_bins.end(), 0);```

### Java

```final double[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
final double[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
final int numItems = weights.length;
final int[] allItems = IntStream.range(0, numItems).toArray();

final double[] binCapacities = {100, 100, 100, 100, 100};
final int numBins = binCapacities.length;
final int[] allBins = IntStream.range(0, numBins).toArray();```

### C#

```double[] Weights = { 48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36 };
double[] Values = { 10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25 };
int NumItems = Weights.Length;
int[] allItems = Enumerable.Range(0, NumItems).ToArray();

double[] BinCapacities = { 100, 100, 100, 100, 100 };
int NumBins = BinCapacities.Length;
int[] allBins = Enumerable.Range(0, NumBins).ToArray();```

The data includes the following:

• `weights`: A vector containing the weights of the items.
• `values`: A vector containing the values of the items.
• `capacities`: A vector containing the capacities of the bins.

In this example, all the bins have the same capacity, but that need not be true in general.

### Declare the MIP solver

The following code declares the MIP solver.

### Python

```solver = pywraplp.Solver.CreateSolver('SCIP')
if solver is None:
print('SCIP solver unavailable.')
return```

### C++

```  std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("SCIP"));
if (!solver) {
LOG(WARNING) << "SCIP solver unavailable.";
return;
}```

### Java

```// Create the linear solver with the SCIP backend.
MPSolver solver = MPSolver.createSolver("SCIP");
if (solver == null) {
System.out.println("Could not create solver SCIP");
return;
}```

### C#

```// Create the linear solver with the SCIP backend.
Solver solver = Solver.CreateSolver("SCIP");
if (solver is null)
{
return;
}```

### Create the variables

The following code creates the variables for the problem.

### Python

```# x[i, b] = 1 if item i is packed in bin b.
x = {}
for i in data['all_items']:
for b in data['all_bins']:
x[i, b] = solver.BoolVar(f'x_{i}_{b}')```

### C++

```// x[i][b] = 1 if item i is packed in bin b.
std::vector<std::vector<const MPVariable*>> x(
num_items, std::vector<const MPVariable*>(num_bins));
for (int i : all_items) {
for (int b : all_bins) {
x[i][b] = solver->MakeBoolVar(absl::StrFormat("x_%d_%d", i, b));
}
}```

### Java

```MPVariable[][] x = new MPVariable[numItems][numBins];
for (int i : allItems) {
for (int b : allBins) {
x[i][b] = solver.makeBoolVar("x_" + i + "_" + b);
}
}```

### C#

```Variable[,] x = new Variable[NumItems, NumBins];
foreach (int i in allItems)
{
foreach (int b in allBins)
{
x[i, b] = solver.MakeBoolVar(\$"x_{i}_{b}");
}
}```

Each `x[(i, j)]` is a 0-1 variable, where `i` is an item and `j` is a bin. In the solution, `x[(i, j)]` will be 1 if item `i` is placed in bin `j`, and 0 otherwise.

### Define the constraints

The following code defines the constraints for the problem:

### Python

```# Each item is assigned to at most one bin.
for i in data['all_items']:
solver.Add(sum(x[i, b] for b in data['all_bins']) <= 1)

# The amount packed in each bin cannot exceed its capacity.
for b in data['all_bins']:
sum(x[i, b] * data['weights'][i]
for i in data['all_items']) <= data['bin_capacities'][b])```

### C++

```// Each item is assigned to at most one bin.
for (int i : all_items) {
LinearExpr sum;
for (int b : all_bins) {
sum += x[i][b];
}
solver->MakeRowConstraint(sum <= 1.0);
}
// The amount packed in each bin cannot exceed its capacity.
for (int b : all_bins) {
LinearExpr bin_weight;
for (int i : all_items) {
bin_weight += LinearExpr(x[i][b]) * weights[i];
}
solver->MakeRowConstraint(bin_weight <= bin_capacities[b]);
}```

### Java

```// Each item is assigned to at most one bin.
for (int i : allItems) {
MPConstraint constraint = solver.makeConstraint(0, 1, "");
for (int b : allBins) {
constraint.setCoefficient(x[i][b], 1);
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : allBins) {
MPConstraint constraint = solver.makeConstraint(0, binCapacities[b], "");
for (int i : allItems) {
constraint.setCoefficient(x[i][b], weights[i]);
}
}```

### C#

```// Each item is assigned to at most one bin.
foreach (int i in allItems)
{
Constraint constraint = solver.MakeConstraint(0, 1, "");
foreach (int b in allBins)
{
constraint.SetCoefficient(x[i, b], 1);
}
}

// The amount packed in each bin cannot exceed its capacity.
foreach (int b in allBins)
{
Constraint constraint = solver.MakeConstraint(0, BinCapacities[b], "");
foreach (int i in allItems)
{
constraint.SetCoefficient(x[i, b], Weights[i]);
}
}```

The constraints are the following:

• Each item can be placed in at most one bin. This constraint is set by requiring the sum of `x[i, j]` over all bins `j` to be less than or equal to 1.
• The total weight packed in each bin can't exceed its capacity. This constraint is set by requiring the sum of the weights of items placed in bin `j` to be less than or equal to the capacity of the bin.

### Define the objective

The following code defines the objective function for the problem, which is the total value of the packed items.

### Python

```# Maximize total value of packed items.
objective = solver.Objective()
for i in data['all_items']:
for b in data['all_bins']:
objective.SetCoefficient(x[i, b], data['values'][i])
objective.SetMaximization()```

### C++

```// Maximize total value of packed items.
MPObjective* const objective = solver->MutableObjective();
LinearExpr objective_value;
for (int i : all_items) {
for (int b : all_bins) {
objective_value += LinearExpr(x[i][b]) * values[i];
}
}
objective->MaximizeLinearExpr(objective_value);```

### Java

```// Maximize total value of packed items.
MPObjective objective = solver.objective();
for (int i : allItems) {
for (int b : allBins) {
objective.setCoefficient(x[i][b], values[i]);
}
}
objective.setMaximization();```

### C#

```Objective objective = solver.Objective();
foreach (int i in allItems)
{
foreach (int b in allBins)
{
objective.SetCoefficient(x[i, b], Values[i]);
}
}
objective.SetMaximization();```

Note that `x[i, j] * data['values'][i]` adds the value of item `i` to the objective if the item is placed in bin `j`. If `i` is not placed in any bin, its value doesn't contribute to the objective.

### Invoke the solver

The following code invokes the solver.

### Python

`status = solver.Solve()`

### C++

`const MPSolver::ResultStatus result_status = solver->Solve();`

### Java

`final MPSolver.ResultStatus status = solver.solve();`

### C#

`Solver.ResultStatus resultStatus = solver.Solve();`

The following code prints the solution to the problem.

### Python

```if status == pywraplp.Solver.OPTIMAL:
print(f'Total packed value: {objective.Value()}')
total_weight = 0
for b in data['all_bins']:
print(f'Bin {b}')
bin_weight = 0
bin_value = 0
for i in data['all_items']:
if x[i, b].solution_value() > 0:
print(
f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
)
bin_weight += data['weights'][i]
bin_value += data['values'][i]
print(f'Packed bin weight: {bin_weight}')
print(f'Packed bin value: {bin_value}\n')
total_weight += bin_weight
print(f'Total packed weight: {total_weight}')
else:
print('The problem does not have an optimal solution.')```

### C++

```if (result_status == MPSolver::OPTIMAL) {
LOG(INFO) << "Total packed value: " << objective->Value();
double total_weight = 0.0;
for (int b : all_bins) {
LOG(INFO) << "Bin " << b;
double bin_weight = 0.0;
double bin_value = 0.0;
for (int i : all_items) {
if (x[i][b]->solution_value() > 0) {
LOG(INFO) << "Item " << i << " weight: " << weights[i]
<< " value: " << values[i];
bin_weight += weights[i];
bin_value += values[i];
}
}
LOG(INFO) << "Packed bin weight: " << bin_weight;
LOG(INFO) << "Packed bin value: " << bin_value;
total_weight += bin_weight;
}
LOG(INFO) << "Total packed weight: " << total_weight;
} else {
LOG(INFO) << "The problem does not have an optimal solution.";
}```

### Java

```// Check that the problem has an optimal solution.
if (status == MPSolver.ResultStatus.OPTIMAL) {
System.out.println("Total packed value: " + objective.value());
double totalWeight = 0;
for (int b : allBins) {
double binWeight = 0;
double binValue = 0;
System.out.println("Bin " + b);
for (int i : allItems) {
if (x[i][b].solutionValue() == 1) {
System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
binWeight += weights[i];
binValue += values[i];
}
}
System.out.println("Packed bin weight: " + binWeight);
System.out.println("Packed bin value: " + binValue);
totalWeight += binWeight;
}
System.out.println("Total packed weight: " + totalWeight);
} else {
System.err.println("The problem does not have an optimal solution.");
}```

### C#

```// Check that the problem has an optimal solution.
if (resultStatus == Solver.ResultStatus.OPTIMAL)
{
Console.WriteLine(\$"Total packed value: {solver.Objective().Value()}");
double TotalWeight = 0.0;
foreach (int b in allBins)
{
double BinWeight = 0.0;
double BinValue = 0.0;
Console.WriteLine("Bin " + b);
foreach (int i in allItems)
{
if (x[i, b].SolutionValue() == 1)
{
Console.WriteLine(\$"Item {i} weight: {Weights[i]} values: {Values[i]}");
BinWeight += Weights[i];
BinValue += Values[i];
}
}
Console.WriteLine("Packed bin weight: " + BinWeight);
Console.WriteLine("Packed bin value: " + BinValue);
TotalWeight += BinWeight;
}
Console.WriteLine("Total packed weight: " + TotalWeight);
}
else
{
Console.WriteLine("The problem does not have an optimal solution!");
}```

For each bin, the code displays the items placed that bin, as well as the bin's total value and weight. The code also displays the overall total value and weight of the packed items.

When you run the program, it displays the following output.

```Total packed value: 395.0
Bin  0

Item 3 - weight: 36  value: 50
Item 13 - weight: 36  value: 30
Packed bin weight: 72
Packed bin value: 80

Bin  1

Item 5 - weight: 48  value: 30
Item 7 - weight: 42  value: 40
Packed bin weight: 90
Packed bin value: 70

Bin  2

Item 1 - weight: 30  value: 30
Item 10 - weight: 30  value: 45
Item 14 - weight: 36  value: 25
Packed bin weight: 96
Packed bin value: 100

Bin  3

Item 2 - weight: 42  value: 25
Item 12 - weight: 42  value: 20
Packed bin weight: 84
Packed bin value: 45

Bin  4

Item 4 - weight: 36  value: 35
Item 8 - weight: 36  value: 30
Item 9 - weight: 24  value: 35
Packed bin weight: 96
Packed bin value: 100

Total packed weight: 438```

### Complete programs

The complete programs for multiple knapsacks are shown below.

### Python

```"""Solve a multiple knapsack problem using a MIP solver."""
from ortools.linear_solver import pywraplp

def main():
data = {}
data['weights'] = [
48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36
]
data['values'] = [
10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25
]
assert len(data['weights']) == len(data['values'])
data['num_items'] = len(data['weights'])
data['all_items'] = range(data['num_items'])

data['bin_capacities'] = [100, 100, 100, 100, 100]
data['num_bins'] = len(data['bin_capacities'])
data['all_bins'] = range(data['num_bins'])

# Create the mip solver with the SCIP backend.
solver = pywraplp.Solver.CreateSolver('SCIP')
if solver is None:
print('SCIP solver unavailable.')
return

# Variables.
# x[i, b] = 1 if item i is packed in bin b.
x = {}
for i in data['all_items']:
for b in data['all_bins']:
x[i, b] = solver.BoolVar(f'x_{i}_{b}')

# Constraints.
# Each item is assigned to at most one bin.
for i in data['all_items']:
solver.Add(sum(x[i, b] for b in data['all_bins']) <= 1)

# The amount packed in each bin cannot exceed its capacity.
for b in data['all_bins']:
sum(x[i, b] * data['weights'][i]
for i in data['all_items']) <= data['bin_capacities'][b])

# Objective.
# Maximize total value of packed items.
objective = solver.Objective()
for i in data['all_items']:
for b in data['all_bins']:
objective.SetCoefficient(x[i, b], data['values'][i])
objective.SetMaximization()

status = solver.Solve()

if status == pywraplp.Solver.OPTIMAL:
print(f'Total packed value: {objective.Value()}')
total_weight = 0
for b in data['all_bins']:
print(f'Bin {b}')
bin_weight = 0
bin_value = 0
for i in data['all_items']:
if x[i, b].solution_value() > 0:
print(
f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
)
bin_weight += data['weights'][i]
bin_value += data['values'][i]
print(f'Packed bin weight: {bin_weight}')
print(f'Packed bin value: {bin_value}\n')
total_weight += bin_weight
print(f'Total packed weight: {total_weight}')
else:
print('The problem does not have an optimal solution.')

if __name__ == '__main__':
main()```

### C++

```// Solve a multiple knapsack problem using a MIP solver.
#include <iostream>
#include <memory>
#include <numeric>
#include <vector>

#include "absl/strings/str_format.h"
#include "ortools/linear_solver/linear_expr.h"
#include "ortools/linear_solver/linear_solver.h"

namespace operations_research {

void MultipleKnapsackMip() {
const std::vector<int> weights = {
{48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36}};
const std::vector<int> values = {
{10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25}};
const int num_items = weights.size();
std::vector<int> all_items(num_items);
std::iota(all_items.begin(), all_items.end(), 0);

const std::vector<int> bin_capacities = {{100, 100, 100, 100, 100}};
const int num_bins = bin_capacities.size();
std::vector<int> all_bins(num_bins);
std::iota(all_bins.begin(), all_bins.end(), 0);

// Create the mip solver with the SCIP backend.
std::unique_ptr<MPSolver> solver(MPSolver::CreateSolver("SCIP"));
if (!solver) {
LOG(WARNING) << "SCIP solver unavailable.";
return;
}

// Variables.
// x[i][b] = 1 if item i is packed in bin b.
std::vector<std::vector<const MPVariable*>> x(
num_items, std::vector<const MPVariable*>(num_bins));
for (int i : all_items) {
for (int b : all_bins) {
x[i][b] = solver->MakeBoolVar(absl::StrFormat("x_%d_%d", i, b));
}
}

// Constraints.
// Each item is assigned to at most one bin.
for (int i : all_items) {
LinearExpr sum;
for (int b : all_bins) {
sum += x[i][b];
}
solver->MakeRowConstraint(sum <= 1.0);
}
// The amount packed in each bin cannot exceed its capacity.
for (int b : all_bins) {
LinearExpr bin_weight;
for (int i : all_items) {
bin_weight += LinearExpr(x[i][b]) * weights[i];
}
solver->MakeRowConstraint(bin_weight <= bin_capacities[b]);
}

// Objective.
// Maximize total value of packed items.
MPObjective* const objective = solver->MutableObjective();
LinearExpr objective_value;
for (int i : all_items) {
for (int b : all_bins) {
objective_value += LinearExpr(x[i][b]) * values[i];
}
}
objective->MaximizeLinearExpr(objective_value);

const MPSolver::ResultStatus result_status = solver->Solve();

if (result_status == MPSolver::OPTIMAL) {
LOG(INFO) << "Total packed value: " << objective->Value();
double total_weight = 0.0;
for (int b : all_bins) {
LOG(INFO) << "Bin " << b;
double bin_weight = 0.0;
double bin_value = 0.0;
for (int i : all_items) {
if (x[i][b]->solution_value() > 0) {
LOG(INFO) << "Item " << i << " weight: " << weights[i]
<< " value: " << values[i];
bin_weight += weights[i];
bin_value += values[i];
}
}
LOG(INFO) << "Packed bin weight: " << bin_weight;
LOG(INFO) << "Packed bin value: " << bin_value;
total_weight += bin_weight;
}
LOG(INFO) << "Total packed weight: " << total_weight;
} else {
LOG(INFO) << "The problem does not have an optimal solution.";
}
}
}  // namespace operations_research

int main(int argc, char** argv) {
operations_research::MultipleKnapsackMip();
return EXIT_SUCCESS;
}```

### Java

```// Solve a multiple knapsack problem using a MIP solver.
import java.util.stream.IntStream;

/** Multiple knapsack problem. */
public class MultipleKnapsackMip {
public static void main(String[] args) {
// Instantiate the data problem.
final double[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
final double[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
final int numItems = weights.length;
final int[] allItems = IntStream.range(0, numItems).toArray();

final double[] binCapacities = {100, 100, 100, 100, 100};
final int numBins = binCapacities.length;
final int[] allBins = IntStream.range(0, numBins).toArray();

// Create the linear solver with the SCIP backend.
MPSolver solver = MPSolver.createSolver("SCIP");
if (solver == null) {
System.out.println("Could not create solver SCIP");
return;
}

// Variables.
MPVariable[][] x = new MPVariable[numItems][numBins];
for (int i : allItems) {
for (int b : allBins) {
x[i][b] = solver.makeBoolVar("x_" + i + "_" + b);
}
}

// Constraints.
// Each item is assigned to at most one bin.
for (int i : allItems) {
MPConstraint constraint = solver.makeConstraint(0, 1, "");
for (int b : allBins) {
constraint.setCoefficient(x[i][b], 1);
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : allBins) {
MPConstraint constraint = solver.makeConstraint(0, binCapacities[b], "");
for (int i : allItems) {
constraint.setCoefficient(x[i][b], weights[i]);
}
}

// Objective.
// Maximize total value of packed items.
MPObjective objective = solver.objective();
for (int i : allItems) {
for (int b : allBins) {
objective.setCoefficient(x[i][b], values[i]);
}
}
objective.setMaximization();

final MPSolver.ResultStatus status = solver.solve();

// Check that the problem has an optimal solution.
if (status == MPSolver.ResultStatus.OPTIMAL) {
System.out.println("Total packed value: " + objective.value());
double totalWeight = 0;
for (int b : allBins) {
double binWeight = 0;
double binValue = 0;
System.out.println("Bin " + b);
for (int i : allItems) {
if (x[i][b].solutionValue() == 1) {
System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
binWeight += weights[i];
binValue += values[i];
}
}
System.out.println("Packed bin weight: " + binWeight);
System.out.println("Packed bin value: " + binValue);
totalWeight += binWeight;
}
System.out.println("Total packed weight: " + totalWeight);
} else {
System.err.println("The problem does not have an optimal solution.");
}
}

private MultipleKnapsackMip() {}
}```

### C#

```// Solve a multiple knapsack problem using a MIP solver.
using System;
using System.Collections.Generic;
using System.Linq;

public class MultipleKnapsackMip
{
public static void Main()
{
// Instantiate the data problem.
double[] Weights = { 48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36 };
double[] Values = { 10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25 };
int NumItems = Weights.Length;
int[] allItems = Enumerable.Range(0, NumItems).ToArray();

double[] BinCapacities = { 100, 100, 100, 100, 100 };
int NumBins = BinCapacities.Length;
int[] allBins = Enumerable.Range(0, NumBins).ToArray();

// Create the linear solver with the SCIP backend.
Solver solver = Solver.CreateSolver("SCIP");
if (solver is null)
{
return;
}

// Variables.
Variable[,] x = new Variable[NumItems, NumBins];
foreach (int i in allItems)
{
foreach (int b in allBins)
{
x[i, b] = solver.MakeBoolVar(\$"x_{i}_{b}");
}
}

// Constraints.
// Each item is assigned to at most one bin.
foreach (int i in allItems)
{
Constraint constraint = solver.MakeConstraint(0, 1, "");
foreach (int b in allBins)
{
constraint.SetCoefficient(x[i, b], 1);
}
}

// The amount packed in each bin cannot exceed its capacity.
foreach (int b in allBins)
{
Constraint constraint = solver.MakeConstraint(0, BinCapacities[b], "");
foreach (int i in allItems)
{
constraint.SetCoefficient(x[i, b], Weights[i]);
}
}

// Objective.
Objective objective = solver.Objective();
foreach (int i in allItems)
{
foreach (int b in allBins)
{
objective.SetCoefficient(x[i, b], Values[i]);
}
}
objective.SetMaximization();

Solver.ResultStatus resultStatus = solver.Solve();

// Check that the problem has an optimal solution.
if (resultStatus == Solver.ResultStatus.OPTIMAL)
{
Console.WriteLine(\$"Total packed value: {solver.Objective().Value()}");
double TotalWeight = 0.0;
foreach (int b in allBins)
{
double BinWeight = 0.0;
double BinValue = 0.0;
Console.WriteLine("Bin " + b);
foreach (int i in allItems)
{
if (x[i, b].SolutionValue() == 1)
{
Console.WriteLine(\$"Item {i} weight: {Weights[i]} values: {Values[i]}");
BinWeight += Weights[i];
BinValue += Values[i];
}
}
Console.WriteLine("Packed bin weight: " + BinWeight);
Console.WriteLine("Packed bin value: " + BinValue);
TotalWeight += BinWeight;
}
Console.WriteLine("Total packed weight: " + TotalWeight);
}
else
{
Console.WriteLine("The problem does not have an optimal solution!");
}
}
}```

## CP SAT solution

The following sections describe how to solve the problem using the CP-SAT solver.

### Import the libraries

The following code imports the required libraries.

### Python

`from ortools.sat.python import cp_model`

### C++

```#include <stdlib.h>

#include <map>
#include <numeric>
#include <tuple>
#include <vector>

#include "absl/strings/str_format.h"
#include "ortools/base/logging.h"
#include "ortools/sat/cp_model.h"
#include "ortools/sat/cp_model.pb.h"
#include "ortools/sat/cp_model_solver.h"```

### Java

```import com.google.ortools.Loader;
import java.util.ArrayList;
import java.util.List;
import java.util.stream.IntStream;```

### C#

```using System;
using System.Collections.Generic;
using System.Linq;

public class MultipleKnapsackSat
{
public static void Main(String[] args)
{
// Instantiate the data problem.
int[] Weights = { 48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36 };
int[] Values = { 10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25 };
int NumItems = Weights.Length;
int[] allItems = Enumerable.Range(0, NumItems).ToArray();

int[] BinCapacities = { 100, 100, 100, 100, 100 };
int NumBins = BinCapacities.Length;
int[] allBins = Enumerable.Range(0, NumBins).ToArray();

// Model.
CpModel model = new CpModel();

// Variables.
ILiteral[,] x = new ILiteral[NumItems, NumBins];
foreach (int i in allItems)
{
foreach (int b in allBins)
{
x[i, b] = model.NewBoolVar(\$"x_{i}_{b}");
}
}

// Constraints.
// Each item is assigned to at most one bin.
foreach (int i in allItems)
{
List<ILiteral> literals = new List<ILiteral>();
foreach (int b in allBins)
{
}
}

// The amount packed in each bin cannot exceed its capacity.
foreach (int b in allBins)
{
List<ILiteral> items = new List<ILiteral>();
foreach (int i in allItems)
{
}
model.Add(LinearExpr.WeightedSum(items, Weights) <= BinCapacities[b]);
}

// Objective.
LinearExprBuilder obj = LinearExpr.NewBuilder();
foreach (int i in allItems)
{
foreach (int b in allBins)
{
}
}
model.Maximize(obj);

// Solve
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);

// Print solution.
// Check that the problem has a feasible solution.
if (status == CpSolverStatus.Optimal || status == CpSolverStatus.Feasible)
{
Console.WriteLine(\$"Total packed value: {solver.ObjectiveValue}");
double TotalWeight = 0.0;
foreach (int b in allBins)
{
double BinWeight = 0.0;
double BinValue = 0.0;
Console.WriteLine(\$"Bin {b}");
foreach (int i in allItems)
{
if (solver.BooleanValue(x[i, b]))
{
Console.WriteLine(\$"Item {i} weight: {Weights[i]} values: {Values[i]}");
BinWeight += Weights[i];
BinValue += Values[i];
}
}
Console.WriteLine("Packed bin weight: " + BinWeight);
Console.WriteLine("Packed bin value: " + BinValue);
TotalWeight += BinWeight;
}
Console.WriteLine("Total packed weight: " + TotalWeight);
}
else
{
Console.WriteLine("No solution found.");
}

Console.WriteLine("Statistics");
Console.WriteLine(\$"  conflicts: {solver.NumConflicts()}");
Console.WriteLine(\$"  branches : {solver.NumBranches()}");
Console.WriteLine(\$"  wall time: {solver.WallTime()}s");
}
}
```

### Declare the model

The following code declares the CP-SAT model.

### Python

`model = cp_model.CpModel()`

### C++

`CpModelBuilder cp_model;`

### Java

`CpModel model = new CpModel();`

### C#

`CpModel model = new CpModel();`

### Create the data

The following code sets up the data for the problem.

### Python

```data = {}
data['weights'] = [
48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36
]
data['values'] = [
10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25
]
assert len(data['weights']) == len(data['values'])
data['num_items'] = len(data['weights'])
data['all_items'] = range(data['num_items'])

data['bin_capacities'] = [100, 100, 100, 100, 100]
data['num_bins'] = len(data['bin_capacities'])
data['all_bins'] = range(data['num_bins'])```

### C++

```const std::vector<int> weights = {
{48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36}};
const std::vector<int> values = {
{10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25}};
const int num_items = static_cast<int>(weights.size());
std::vector<int> all_items(num_items);
std::iota(all_items.begin(), all_items.end(), 0);

const std::vector<int> bin_capacities = {{100, 100, 100, 100, 100}};
const int num_bins = static_cast<int>(bin_capacities.size());
std::vector<int> all_bins(num_bins);
std::iota(all_bins.begin(), all_bins.end(), 0);```

### Java

```final int[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
final int[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
final int numItems = weights.length;
final int[] allItems = IntStream.range(0, numItems).toArray();

final int[] binCapacities = {100, 100, 100, 100, 100};
final int numBins = binCapacities.length;
final int[] allBins = IntStream.range(0, numBins).toArray();```

### C#

```int[] Weights = { 48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36 };
int[] Values = { 10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25 };
int NumItems = Weights.Length;
int[] allItems = Enumerable.Range(0, NumItems).ToArray();

int[] BinCapacities = { 100, 100, 100, 100, 100 };
int NumBins = BinCapacities.Length;
int[] allBins = Enumerable.Range(0, NumBins).ToArray();```

The `costs` array corresponds to the table of costs for assigning workers to tasks, shown above.

### Create the variables

The following code creates binary integer variables for the problem.

### Python

```# x[i, b] = 1 if item i is packed in bin b.
x = {}
for i in data['all_items']:
for b in data['all_bins']:
x[i, b] = model.NewBoolVar(f'x_{i}_{b}')```

### C++

```// x[i, b] = 1 if item i is packed in bin b.
std::map<std::tuple<int, int>, BoolVar> x;
for (int i : all_items) {
for (int b : all_bins) {
auto key = std::make_tuple(i, b);
x[key] = cp_model.NewBoolVar().WithName(absl::StrFormat("x_%d_%d", i, b));
}
}```

### Java

```Literal[][] x = new Literal[numItems][numBins];
for (int i : allItems) {
for (int b : allBins) {
x[i][b] = model.newBoolVar("x_" + i + "_" + b);
}
}```

### C#

```ILiteral[,] x = new ILiteral[NumItems, NumBins];
foreach (int i in allItems)
{
foreach (int b in allBins)
{
x[i, b] = model.NewBoolVar(\$"x_{i}_{b}");
}
}```

### Create the constraints

The following code creates the constraints for the problem.

### Python

```# Each item is assigned to at most one bin.
for i in data['all_items']:
model.AddAtMostOne(x[i, b] for b in data['all_bins'])

# The amount packed in each bin cannot exceed its capacity.
for b in data['all_bins']:
sum(x[i, b] * data['weights'][i]
for i in data['all_items']) <= data['bin_capacities'][b])```

### C++

```// Each item is assigned to at most one bin.
for (int i : all_items) {
std::vector<BoolVar> copies;
for (int b : all_bins) {
copies.push_back(x[std::make_tuple(i, b)]);
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : all_bins) {
LinearExpr bin_weight;
for (int i : all_items) {
bin_weight += x[std::make_tuple(i, b)] * weights[i];
}
}```

### Java

```// Each item is assigned to at most one bin.
for (int i : allItems) {
List<Literal> bins = new ArrayList<>();
for (int b : allBins) {
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : allBins) {
LinearExprBuilder load = LinearExpr.newBuilder();
for (int i : allItems) {
}
}```

### C#

```// Each item is assigned to at most one bin.
foreach (int i in allItems)
{
List<ILiteral> literals = new List<ILiteral>();
foreach (int b in allBins)
{
}
}

// The amount packed in each bin cannot exceed its capacity.
foreach (int b in allBins)
{
List<ILiteral> items = new List<ILiteral>();
foreach (int i in allItems)
{
}
model.Add(LinearExpr.WeightedSum(items, Weights) <= BinCapacities[b]);
}```

### Create the objective function

The following code creates the objective function for the problem.

### Python

```# Maximize total value of packed items.
objective = []
for i in data['all_items']:
for b in data['all_bins']:
objective.append(
cp_model.LinearExpr.Term(x[i, b], data['values'][i]))
model.Maximize(cp_model.LinearExpr.Sum(objective))```

### C++

```// Maximize total value of packed items.
LinearExpr objective;
for (int i : all_items) {
for (int b : all_bins) {
objective += x[std::make_tuple(i, b)] * values[i];
}
}
cp_model.Maximize(objective);```

### Java

```// Maximize total value of packed items.
LinearExprBuilder obj = LinearExpr.newBuilder();
for (int i : allItems) {
for (int b : allBins) {
}
}
model.maximize(obj);```

### C#

```LinearExprBuilder obj = LinearExpr.NewBuilder();
foreach (int i in allItems)
{
foreach (int b in allBins)
{
}
}
model.Maximize(obj);```

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

### Invoke the solver

The following code invokes the solver.

### Python

```solver = cp_model.CpSolver()
status = solver.Solve(model)```

### C++

`const CpSolverResponse response = Solve(cp_model.Build());`

### Java

```CpSolver solver = new CpSolver();
final CpSolverStatus status = solver.solve(model);```

### C#

```CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);```

The following code prints the solution to the problem.

### Python

```if status == cp_model.OPTIMAL:
print(f'Total packed value: {solver.ObjectiveValue()}')
total_weight = 0
for b in data['all_bins']:
print(f'Bin {b}')
bin_weight = 0
bin_value = 0
for i in data['all_items']:
if solver.Value(x[i, b]) > 0:
print(
f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
)
bin_weight += data['weights'][i]
bin_value += data['values'][i]
print(f'Packed bin weight: {bin_weight}')
print(f'Packed bin value: {bin_value}\n')
total_weight += bin_weight
print(f'Total packed weight: {total_weight}')
else:
print('The problem does not have an optimal solution.')```

### C++

```if (response.status() == CpSolverStatus::OPTIMAL ||
response.status() == CpSolverStatus::FEASIBLE) {
LOG(INFO) << "Total packed value: " << response.objective_value();
double total_weight = 0.0;
for (int b : all_bins) {
LOG(INFO) << "Bin " << b;
double bin_weight = 0.0;
double bin_value = 0.0;
for (int i : all_items) {
auto key = std::make_tuple(i, b);
if (SolutionIntegerValue(response, x[key]) > 0) {
LOG(INFO) << "Item " << i << " weight: " << weights[i]
<< " value: " << values[i];
bin_weight += weights[i];
bin_value += values[i];
}
}
LOG(INFO) << "Packed bin weight: " << bin_weight;
LOG(INFO) << "Packed bin value: " << bin_value;
total_weight += bin_weight;
}
LOG(INFO) << "Total packed weight: " << total_weight;
} else {
LOG(INFO) << "The problem does not have an optimal solution.";
}```

### Java

```// Check that the problem has an optimal solution.
if (status == CpSolverStatus.OPTIMAL) {
System.out.println("Total packed value: " + solver.objectiveValue());
long totalWeight = 0;
for (int b : allBins) {
long binWeight = 0;
long binValue = 0;
System.out.println("Bin " + b);
for (int i : allItems) {
if (solver.booleanValue(x[i][b])) {
System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
binWeight += weights[i];
binValue += values[i];
}
}
System.out.println("Packed bin weight: " + binWeight);
System.out.println("Packed bin value: " + binValue);
totalWeight += binWeight;
}
System.out.println("Total packed weight: " + totalWeight);
} else {
System.err.println("The problem does not have an optimal solution.");
}```

### C#

```// Check that the problem has a feasible solution.
if (status == CpSolverStatus.Optimal || status == CpSolverStatus.Feasible)
{
Console.WriteLine(\$"Total packed value: {solver.ObjectiveValue}");
double TotalWeight = 0.0;
foreach (int b in allBins)
{
double BinWeight = 0.0;
double BinValue = 0.0;
Console.WriteLine(\$"Bin {b}");
foreach (int i in allItems)
{
if (solver.BooleanValue(x[i, b]))
{
Console.WriteLine(\$"Item {i} weight: {Weights[i]} values: {Values[i]}");
BinWeight += Weights[i];
BinValue += Values[i];
}
}
Console.WriteLine("Packed bin weight: " + BinWeight);
Console.WriteLine("Packed bin value: " + BinValue);
TotalWeight += BinWeight;
}
Console.WriteLine("Total packed weight: " + TotalWeight);
}
else
{
Console.WriteLine("No solution found.");
}```

Here is the output of the program.

```Total packed value: 395.0
Bin  0

Item 3 - weight: 36  value: 50
Item 13 - weight: 36  value: 30
Packed bin weight: 72
Packed bin value: 80

Bin  1

Item 5 - weight: 48  value: 30
Item 7 - weight: 42  value: 40
Packed bin weight: 90
Packed bin value: 70

Bin  2

Item 1 - weight: 30  value: 30
Item 10 - weight: 30  value: 45
Item 14 - weight: 36  value: 25
Packed bin weight: 96
Packed bin value: 100

Bin  3

Item 2 - weight: 42  value: 25
Item 12 - weight: 42  value: 20
Packed bin weight: 84
Packed bin value: 45

Bin  4

Item 4 - weight: 36  value: 35
Item 8 - weight: 36  value: 30
Item 9 - weight: 24  value: 35
Packed bin weight: 96
Packed bin value: 100

Total packed weight: 438```

### Complete programs

Here are the complete programs for the CP-SAT solution.

### Python

```"""Solves a multiple knapsack problem using the CP-SAT solver."""
from ortools.sat.python import cp_model

def main():
data = {}
data['weights'] = [
48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36
]
data['values'] = [
10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25
]
assert len(data['weights']) == len(data['values'])
data['num_items'] = len(data['weights'])
data['all_items'] = range(data['num_items'])

data['bin_capacities'] = [100, 100, 100, 100, 100]
data['num_bins'] = len(data['bin_capacities'])
data['all_bins'] = range(data['num_bins'])

model = cp_model.CpModel()

# Variables.
# x[i, b] = 1 if item i is packed in bin b.
x = {}
for i in data['all_items']:
for b in data['all_bins']:
x[i, b] = model.NewBoolVar(f'x_{i}_{b}')

# Constraints.
# Each item is assigned to at most one bin.
for i in data['all_items']:
model.AddAtMostOne(x[i, b] for b in data['all_bins'])

# The amount packed in each bin cannot exceed its capacity.
for b in data['all_bins']:
sum(x[i, b] * data['weights'][i]
for i in data['all_items']) <= data['bin_capacities'][b])

# Objective.
# Maximize total value of packed items.
objective = []
for i in data['all_items']:
for b in data['all_bins']:
objective.append(
cp_model.LinearExpr.Term(x[i, b], data['values'][i]))
model.Maximize(cp_model.LinearExpr.Sum(objective))

solver = cp_model.CpSolver()
status = solver.Solve(model)

if status == cp_model.OPTIMAL:
print(f'Total packed value: {solver.ObjectiveValue()}')
total_weight = 0
for b in data['all_bins']:
print(f'Bin {b}')
bin_weight = 0
bin_value = 0
for i in data['all_items']:
if solver.Value(x[i, b]) > 0:
print(
f"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}"
)
bin_weight += data['weights'][i]
bin_value += data['values'][i]
print(f'Packed bin weight: {bin_weight}')
print(f'Packed bin value: {bin_value}\n')
total_weight += bin_weight
print(f'Total packed weight: {total_weight}')
else:
print('The problem does not have an optimal solution.')

if __name__ == '__main__':
main()```

### C++

```// Solves a multiple knapsack problem using the CP-SAT solver.
#include <stdlib.h>

#include <map>
#include <numeric>
#include <tuple>
#include <vector>

#include "absl/strings/str_format.h"
#include "ortools/base/logging.h"
#include "ortools/sat/cp_model.h"
#include "ortools/sat/cp_model.pb.h"
#include "ortools/sat/cp_model_solver.h"

namespace operations_research {
namespace sat {

void MultipleKnapsackSat() {
const std::vector<int> weights = {
{48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36}};
const std::vector<int> values = {
{10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25}};
const int num_items = static_cast<int>(weights.size());
std::vector<int> all_items(num_items);
std::iota(all_items.begin(), all_items.end(), 0);

const std::vector<int> bin_capacities = {{100, 100, 100, 100, 100}};
const int num_bins = static_cast<int>(bin_capacities.size());
std::vector<int> all_bins(num_bins);
std::iota(all_bins.begin(), all_bins.end(), 0);

CpModelBuilder cp_model;

// Variables.
// x[i, b] = 1 if item i is packed in bin b.
std::map<std::tuple<int, int>, BoolVar> x;
for (int i : all_items) {
for (int b : all_bins) {
auto key = std::make_tuple(i, b);
x[key] = cp_model.NewBoolVar().WithName(absl::StrFormat("x_%d_%d", i, b));
}
}

// Constraints.
// Each item is assigned to at most one bin.
for (int i : all_items) {
std::vector<BoolVar> copies;
for (int b : all_bins) {
copies.push_back(x[std::make_tuple(i, b)]);
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : all_bins) {
LinearExpr bin_weight;
for (int i : all_items) {
bin_weight += x[std::make_tuple(i, b)] * weights[i];
}
}

// Objective.
// Maximize total value of packed items.
LinearExpr objective;
for (int i : all_items) {
for (int b : all_bins) {
objective += x[std::make_tuple(i, b)] * values[i];
}
}
cp_model.Maximize(objective);

const CpSolverResponse response = Solve(cp_model.Build());

if (response.status() == CpSolverStatus::OPTIMAL ||
response.status() == CpSolverStatus::FEASIBLE) {
LOG(INFO) << "Total packed value: " << response.objective_value();
double total_weight = 0.0;
for (int b : all_bins) {
LOG(INFO) << "Bin " << b;
double bin_weight = 0.0;
double bin_value = 0.0;
for (int i : all_items) {
auto key = std::make_tuple(i, b);
if (SolutionIntegerValue(response, x[key]) > 0) {
LOG(INFO) << "Item " << i << " weight: " << weights[i]
<< " value: " << values[i];
bin_weight += weights[i];
bin_value += values[i];
}
}
LOG(INFO) << "Packed bin weight: " << bin_weight;
LOG(INFO) << "Packed bin value: " << bin_value;
total_weight += bin_weight;
}
LOG(INFO) << "Total packed weight: " << total_weight;
} else {
LOG(INFO) << "The problem does not have an optimal solution.";
}

// Statistics.
LOG(INFO) << "Statistics";
LOG(INFO) << CpSolverResponseStats(response);
}
}  // namespace sat
}  // namespace operations_research

int main() {
operations_research::sat::MultipleKnapsackSat();
return EXIT_SUCCESS;
}```

### Java

```// Solves a multiple knapsack problem using the CP-SAT solver.
import java.util.ArrayList;
import java.util.List;
import java.util.stream.IntStream;

/** Sample showing how to solve a multiple knapsack problem. */
public class MultipleKnapsackSat {
public static void main(String[] args) {
// Instantiate the data problem.
final int[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
final int[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
final int numItems = weights.length;
final int[] allItems = IntStream.range(0, numItems).toArray();

final int[] binCapacities = {100, 100, 100, 100, 100};
final int numBins = binCapacities.length;
final int[] allBins = IntStream.range(0, numBins).toArray();

CpModel model = new CpModel();

// Variables.
Literal[][] x = new Literal[numItems][numBins];
for (int i : allItems) {
for (int b : allBins) {
x[i][b] = model.newBoolVar("x_" + i + "_" + b);
}
}

// Constraints.
// Each item is assigned to at most one bin.
for (int i : allItems) {
List<Literal> bins = new ArrayList<>();
for (int b : allBins) {
}
}

// The amount packed in each bin cannot exceed its capacity.
for (int b : allBins) {
LinearExprBuilder load = LinearExpr.newBuilder();
for (int i : allItems) {
}
}

// Objective.
// Maximize total value of packed items.
LinearExprBuilder obj = LinearExpr.newBuilder();
for (int i : allItems) {
for (int b : allBins) {
}
}
model.maximize(obj);

CpSolver solver = new CpSolver();
final CpSolverStatus status = solver.solve(model);

// Check that the problem has an optimal solution.
if (status == CpSolverStatus.OPTIMAL) {
System.out.println("Total packed value: " + solver.objectiveValue());
long totalWeight = 0;
for (int b : allBins) {
long binWeight = 0;
long binValue = 0;
System.out.println("Bin " + b);
for (int i : allItems) {
if (solver.booleanValue(x[i][b])) {
System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
binWeight += weights[i];
binValue += values[i];
}
}
System.out.println("Packed bin weight: " + binWeight);
System.out.println("Packed bin value: " + binValue);
totalWeight += binWeight;
}
System.out.println("Total packed weight: " + totalWeight);
} else {
System.err.println("The problem does not have an optimal solution.");
}
}

private MultipleKnapsackSat() {}
}```

### C#

```// Solves a multiple knapsack problem using the CP-SAT solver.
using System;
using System.Collections.Generic;
using System.Linq;

public class MultipleKnapsackSat
{
public static void Main(String[] args)
{
// Instantiate the data problem.
int[] Weights = { 48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36 };
int[] Values = { 10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25 };
int NumItems = Weights.Length;
int[] allItems = Enumerable.Range(0, NumItems).ToArray();

int[] BinCapacities = { 100, 100, 100, 100, 100 };
int NumBins = BinCapacities.Length;
int[] allBins = Enumerable.Range(0, NumBins).ToArray();

// Model.
CpModel model = new CpModel();

// Variables.
ILiteral[,] x = new ILiteral[NumItems, NumBins];
foreach (int i in allItems)
{
foreach (int b in allBins)
{
x[i, b] = model.NewBoolVar(\$"x_{i}_{b}");
}
}

// Constraints.
// Each item is assigned to at most one bin.
foreach (int i in allItems)
{
List<ILiteral> literals = new List<ILiteral>();
foreach (int b in allBins)
{
}
}

// The amount packed in each bin cannot exceed its capacity.
foreach (int b in allBins)
{
List<ILiteral> items = new List<ILiteral>();
foreach (int i in allItems)
{
}
model.Add(LinearExpr.WeightedSum(items, Weights) <= BinCapacities[b]);
}

// Objective.
LinearExprBuilder obj = LinearExpr.NewBuilder();
foreach (int i in allItems)
{
foreach (int b in allBins)
{
}
}
model.Maximize(obj);

// Solve
CpSolver solver = new CpSolver();
CpSolverStatus status = solver.Solve(model);

// Print solution.
// Check that the problem has a feasible solution.
if (status == CpSolverStatus.Optimal || status == CpSolverStatus.Feasible)
{
Console.WriteLine(\$"Total packed value: {solver.ObjectiveValue}");
double TotalWeight = 0.0;
foreach (int b in allBins)
{
double BinWeight = 0.0;
double BinValue = 0.0;
Console.WriteLine(\$"Bin {b}");
foreach (int i in allItems)
{
if (solver.BooleanValue(x[i, b]))
{
Console.WriteLine(\$"Item {i} weight: {Weights[i]} values: {Values[i]}");
BinWeight += Weights[i];
BinValue += Values[i];
}
}
Console.WriteLine("Packed bin weight: " + BinWeight);
Console.WriteLine("Packed bin value: " + BinValue);
TotalWeight += BinWeight;
}
Console.WriteLine("Total packed weight: " + TotalWeight);
}
else
{
Console.WriteLine("No solution found.");
}

Console.WriteLine("Statistics");
Console.WriteLine(\$"  conflicts: {solver.NumConflicts()}");
Console.WriteLine(\$"  branches : {solver.NumBranches()}");
Console.WriteLine(\$"  wall time: {solver.WallTime()}s");
}
}```
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]