class

ORTools::Sat::Model

Model class contains all the variables and constraints that define the problem The variables and constraints are stored in the CpModelProto object The Class also provides all the methods used to add variables and define constraints

Constructors

new

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Instance methods

add_all_diff(vars : Array(Expressible))

Ensures all variables given take completely distinct values from each other

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add_at_most_one(vars : Array(BoolVar))

Forces at most one of the provided boolean variables to be true

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add_bool_and(vars : Array(BoolVar), enforcement_literals = [] of BoolVar)

Forces all of the boolean variables to be true

When enforcement_literals are provided, this constraint will only be followed if all literals are true.

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add_bool_or(vars : Array(BoolVar), enforcement_literals = [] of BoolVar)

Forces at least one of the boolean variables to be true

When enforcement_literals are provided, this constraint will only be followed if all literals are true.

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add_bool_xor(vars : Array(BoolVar))

Forces an odd number of the provided boolean variables to be true

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add_constraint(constraint : LinearConstraint)

Implements a linear constraint in the form of a <= b or a == b.

E.g. model.add_constraint( 2x + 3y <= 44)

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add_exactly_one(vars : Array(BoolVar))

Forces exactly one of the provided boolean variables to be true

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add_int_div(target : Expressible, exprs : Array(Expressible))

Forces the target to equal exprs[0] / exprs[1]. The division is rounded towards zero. For exact integer division, use product constraint and place the target as an expr. E.g. a = b * target

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add_int_mod(target : Expressible, exprs : Array(Expressible))

Forces the target to be equal exprs[0] % exprs[1].

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add_int_prod(target : Expressible, exprs : Array(Expressible))

Forces the target to be equal to the product of the exprs. The product must fit in an int64 or the model will be invalid.

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add_max(target : Expressible, exprs : Array(Expressible))

Forces target to equal the max of all exprs

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add_min(target : Expressible, exprs : Array(Expressible))

Forces target to equal the min of all exprs

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add_none(vars : Array(BoolVar), enforcement_literals = [] of BoolVar)

Forces all BoolVars provided to be false

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add_not_all(vars : Array(BoolVar), enforcement_literals = [] of BoolVar)

Forces at least one of the provided boolean variables to be false

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maximize(expr : Expressible, domain = [] of Int64, offset : Float64 | Nil = nil, scaling_factor : Float64 | Nil = nil)

Create an objective to maximize

The solver only minimizes, so maximization is expressed by minimizing the negated expression. The reported objective_value is scaling_factor * (offset + sum(coeffs*vars)), so to keep it equal to the user's expression both the offset and the scaling factor must be negated as well (scaling_factor defaults to 1 when unset).

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minimize(expr : Expressible, domain = [] of Int64, offset : Float64 | Nil = nil, scaling_factor : Float64 | Nil = nil)

Create an objective to minimize

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new_bool_var(name = "")

Provides a BoolVar for boolean variables

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new_int_var(min : Int, max : Int, name = "")

Provides an IntVar for integer variables

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set_to_bool_and(target : BoolVar, vars : Array(BoolVar))

Creates and equivelence between target variable and the result of the logical AND of the vars

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set_to_bool_or(target : BoolVar, vars : Array(BoolVar))

Creates and equivelence between target variable and the result of the logical OR of the vars

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solve(max_time_in_seconds : Number | Nil = nil) : Solution

Attempts to solve

When max_time_in_seconds is provided, the search is capped at that many seconds. If the limit is reached the solver returns the best solution found so far (status FEASIBLE, or UNKNOWN if none was found) rather than running until it can prove optimality. Omitting it keeps the default behavior of searching until optimal/infeasible.

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valid?

Returns true when the model passes OR-Tools validation.

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validate

Validates the model against OR-Tools' own model checker.

Returns an empty string when the model is valid, otherwise a human readable description of the first problem found (e.g. an out-of-range variable reference). This is a cheap way to catch a malformed model before spending time in #solve.

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