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Class LinearOptimizationEngine

Mesin yang digunakan untuk membuat model dan menyelesaikan program linear. Contoh di bawah ini menyelesaikan program linear berikut:

Dua variabel, x dan y:
0 ≤ x ≤ 10
0 ≤ y ≤ 5

Batasan:
0 ≤ 2 * x + 5 * y ≤ 10
0 ≤ 10 * x + 3 * y ≤ 20

Tujuan:
Memaksimalkan x + y

var engine = LinearOptimizationService.createEngine();

// Add variables, constraints and define the objective with addVariable(), addConstraint(), etc
// Add two variables, 0 <= x <= 10 and 0 <= y <= 5
engine.addVariable('x', 0, 10);
engine.addVariable('y', 0, 5);

// Create the constraint: 0 <= 2 * x + 5 * y <= 10
var constraint = engine.addConstraint(0, 10);
constraint.setCoefficient('x', 2);
constraint.setCoefficient('y', 5);

// Create the constraint: 0 <= 10 * x + 3 * y <= 20
var constraint = engine.addConstraint(0, 20);
constraint.setCoefficient('x', 10);
constraint.setCoefficient('y', 3);

// Set the objective to be x + y
engine.setObjectiveCoefficient('x', 1);
engine.setObjectiveCoefficient('y', 1);

// Engine should maximize the objective
engine.setMaximization();

// Solve the linear program
var solution = engine.solve();
if (!solution.isValid()) {
  Logger.log('No solution ' + solution.getStatus());
} else {
  Logger.log('Value of x: ' + solution.getVariableValue('x'));
  Logger.log('Value of y: ' + solution.getVariableValue('y'));
}

Metode

Metode	Jenis hasil yang ditampilkan	Deskripsi singkat
`addConstraint(lowerBound, upperBound)`	`LinearOptimizationConstraint`	Menambahkan batasan linear baru dalam model.
`addConstraints(lowerBounds, upperBounds, variableNames, coefficients)`	`LinearOptimizationEngine`	Menambahkan batasan dalam batch ke model.
`addVariable(name, lowerBound, upperBound)`	`LinearOptimizationEngine`	Menambahkan variabel berkelanjutan baru ke model.
`addVariable(name, lowerBound, upperBound, type)`	`LinearOptimizationEngine`	Menambahkan variabel baru ke model.
`addVariable(name, lowerBound, upperBound, type, objectiveCoefficient)`	`LinearOptimizationEngine`	Menambahkan variabel baru ke model.
`addVariables(names, lowerBounds, upperBounds, types, objectiveCoefficients)`	`LinearOptimizationEngine`	Menambahkan variabel dalam batch ke model.
`setMaximization()`	`LinearOptimizationEngine`	Menetapkan arah pengoptimalan untuk memaksimalkan fungsi tujuan linear.
`setMinimization()`	`LinearOptimizationEngine`	Menetapkan arah pengoptimalan untuk meminimalkan fungsi tujuan linear.
`setObjectiveCoefficient(variableName, coefficient)`	`LinearOptimizationEngine`	Menetapkan koefisien variabel dalam fungsi tujuan linear.
`solve()`	`LinearOptimizationSolution`	Selesaikan program linear saat ini dengan batas waktu default 30 detik.
`solve(seconds)`	`LinearOptimizationSolution`	Selesaikan program linear saat ini.

Dokumentasi mendetail

`addConstraint(lowerBound, upperBound)`

Menambahkan batasan linear baru dalam model. Batas atas dan bawah batasan ditentukan pada waktu pembuatan. Koefisien untuk variabel ditentukan melalui panggilan ke LinearOptimizationConstraint.setCoefficient(variableName, coefficient).

var engine = LinearOptimizationService.createEngine();

// Create a linear constraint with the bounds 0 and 10
var constraint = engine.addConstraint(0, 10);

// Create a variable so we can add it to the constraint
engine.addVariable('x', 0, 5);

// Set the coefficient of the variable in the constraint. The constraint is now:
// 0 <= 2 * x <= 5
constraint.setCoefficient('x', 2);

Parameter

Nama	Jenis	Deskripsi
`lowerBound`	`Number`	batas bawah batasan
`upperBound`	`Number`	batas atas batasan

Return

LinearOptimizationConstraint — batasan dibuat

`addConstraints(lowerBounds, upperBounds, variableNames, coefficients)`

Menambahkan batasan dalam batch ke model.

var engine = LinearOptimizationService.createEngine();

// Add a boolean variable 'x' (integer >= 0 and <= 1) and a real (continuous >= 0 and <= 100)
variable 'y'.
engine.addVariables(['x', 'y'], [0, 0], [1, 100],
    [LinearOptimizationService.VariableType.INTEGER,
        LinearOptimizationService.VariableType.CONTINUOUS]);

// Adds two constraints:
//   0 <= x + y <= 3
//   1 <= 10 * x - y <= 5
engine.addConstraints([0.0, 1.0], [3.0, 5.0], [['x', 'y'], ['x', 'y']], [[1, 1], [10, -1]]);

Parameter

Nama	Jenis	Deskripsi
`lowerBounds`	`Number[]`	batas bawah batasan
`upperBounds`	`Number[]`	batas atas batasan
`variableNames`	`String[][]`	nama variabel yang koefisiennya ditetapkan
`coefficients`	`Number[][]`	koefisien yang ditetapkan

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`addVariable(name, lowerBound, upperBound)`

Menambahkan variabel berkelanjutan baru ke model. Variabel direferensikan dengan namanya. Jenisnya ditetapkan ke VariableType.CONTINUOUS.

var engine = LinearOptimizationService.createEngine();
var constraint = engine.addConstraint(0, 10);

// Add a boolean variable (integer >= 0 and <= 1)
engine.addVariable('x', 0, 1, LinearOptimizationService.VariableType.INTEGER);

// Add a real (continuous) variable. Notice the lack of type specification.
engine.addVariable('y', 0, 100);

Parameter

Nama	Jenis	Deskripsi
`name`	`String`	nama unik variabel
`lowerBound`	`Number`	batas bawah variabel
`upperBound`	`Number`	batas atas variabel

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`addVariable(name, lowerBound, upperBound, type)`

Menambahkan variabel baru ke model. Variabel direferensikan dengan namanya.

var engine = LinearOptimizationService.createEngine();
var constraint = engine.addConstraint(0, 10);

// Add a boolean variable (integer >= 0 and <= 1)
engine.addVariable('x', 0, 1, LinearOptimizationService.VariableType.INTEGER);

// Add a real (continuous) variable
engine.addVariable('y', 0, 100, LinearOptimizationService.VariableType.CONTINUOUS);

Parameter

Nama	Jenis	Deskripsi
`name`	`String`	nama unik variabel
`lowerBound`	`Number`	batas bawah variabel
`upperBound`	`Number`	batas atas variabel
`type`	`VariableType`	jenis variabel, dapat berupa salah satu dari `VariableType`

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`addVariable(name, lowerBound, upperBound, type, objectiveCoefficient)`

Menambahkan variabel baru ke model. Variabel direferensikan dengan namanya.

var engine = LinearOptimizationService.createEngine();
var constraint = engine.addConstraint(0, 10);

// Add a boolean variable (integer >= 0 and <= 1)
engine.addVariable('x', 0, 1, LinearOptimizationService.VariableType.INTEGER, 2);
// The objective is now 2 * x.

// Add a real (continuous) variable
engine.addVariable('y', 0, 100, LinearOptimizationService.VariableType.CONTINUOUS, -5);
// The objective is now 2 * x - 5 * y.

Parameter

Nama	Jenis	Deskripsi
`name`	`String`	nama unik variabel
`lowerBound`	`Number`	batas bawah variabel
`upperBound`	`Number`	batas atas variabel
`type`	`VariableType`	jenis variabel, dapat berupa salah satu dari `VariableType`
`objectiveCoefficient`	`Number`	koefisien objektif variabel

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`addVariables(names, lowerBounds, upperBounds, types, objectiveCoefficients)`

Menambahkan variabel dalam batch ke model. Variabel-variabel dirujuk oleh namanya.

var engine = LinearOptimizationService.createEngine();

// Add a boolean variable 'x' (integer >= 0 and <= 1) and a real (continuous >=0 and <= 100)
// variable 'y'.
engine.addVariables(['x', 'y'], [0, 0], [1, 100],
    [LinearOptimizationService.VariableType.INTEGER,
        LinearOptimizationService.VariableType.CONTINUOUS]);

Parameter

Nama	Jenis	Deskripsi
`names`	`String[]`	nama unik variabel
`lowerBounds`	`Number[]`	batas bawah variabel
`upperBounds`	`Number[]`	batas atas variabel
`types`	`VariableType[]`	jenis variabel, dapat berupa salah satu dari `VariableType`
`objectiveCoefficients`	`Number[]`	koefisien objektif dari variabel

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`setMaximization()`

Menetapkan arah pengoptimalan untuk memaksimalkan fungsi tujuan linear.

var engine = LinearOptimizationService.createEngine();

// Add a real (continuous) variable. Notice the lack of type specification.
engine.addVariable('y', 0, 100);

// Set the coefficient of 'y' in the objective.
// The objective is now 5 * y
engine.setObjectiveCoefficient('y', 5);

// We want to maximize.
engine.setMaximization();

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`setMinimization()`

Menetapkan arah pengoptimalan untuk meminimalkan fungsi tujuan linear.

var engine = LinearOptimizationService.createEngine();

// Add a real (continuous) variable. Notice the lack of type specification.
engine.addVariable('y', 0, 100);

// Set the coefficient of 'y' in the objective.
// The objective is now 5 * y
engine.setObjectiveCoefficient('y', 5);

// We want to minimize
engine.setMinimization();

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`setObjectiveCoefficient(variableName, coefficient)`

Menetapkan koefisien variabel dalam fungsi tujuan linear.

var engine = LinearOptimizationService.createEngine();

// Add a real (continuous) variable. Notice the lack of type specification.
engine.addVariable('y', 0, 100);

// Set the coefficient of 'y' in the objective.
// The objective is now 5 * y
engine.setObjectiveCoefficient('y', 5);

Parameter

Nama	Jenis	Deskripsi
`variableName`	`String`	nama variabel yang koefisiennya sedang ditetapkan
`coefficient`	`Number`	koefisien variabel dalam fungsi objektif

Return

LinearOptimizationEngine — mesin pengoptimalan linear

`solve()`

Selesaikan program linear saat ini dengan batas waktu default 30 detik. Menampilkan solusi yang ditemukan.

var engine = LinearOptimizationService.createEngine();

// Add variables, constraints and define the objective with addVariable(), addConstraint(), etc
engine.addVariable('x', 0, 10);

// ...

// Solve the linear program
var solution = engine.solve();
if (!solution.isValid()) {
  throw 'No solution ' + solution.getStatus();
}
Logger.log('Value of x: ' + solution.getVariableValue('x'));

Return

LinearOptimizationSolution — solusi pengoptimalan

`solve(seconds)`

Selesaikan program linear saat ini. Menampilkan solusi yang ditemukan, dan apakah solusi tersebut merupakan solusi optimal.

var engine = LinearOptimizationService.createEngine();

// Add variables, constraints and define the objective with addVariable(), addConstraint(), etc
engine.addVariable('x', 0, 10);

// ...

// Solve the linear program
var solution = engine.solve(300);
if (!solution.isValid()) {
  throw 'No solution ' + solution.getStatus();
}
Logger.log('Value of x: ' + solution.getVariableValue('x'));

Parameter

Nama	Jenis	Deskripsi
`seconds`	`Number`	batas waktu untuk memecahkan masalah, dalam hitungan detik; tenggat waktu maksimum adalah 300 detik

Return

LinearOptimizationSolution — solusi pengoptimalan