labscheduler.solvers.cp_solver module

A solver implementation using google OR-tools to model and solve the JSSP as a constraint program(CP)

class labscheduler.solvers.cp_solver.CPSolver

Bases: JSSPSolver

_abc_impl = <_abc._abc_data object>

static add_group_constraints(cp: CpModel, vars_: CPVariables, inst: JSSP)

Not strictly necessary in the sense of the JSSP definition. We define a group of operations as an operation together with all its directly preceding options which must be more than one. The constraints added in this function enforce all operations of a group to be started before another one can be started.

static add_load_while_processing_constraints(cp: CpModel, vars_: CPVariables, inst: JSSP)

static add_precedence_constraints(cp: CpModel, vars_: CPVariables, inst: JSSP)

static add_processing_capacity_constraints(cp: CpModel, vars_: CPVariables, inst: JSSP)

static add_soft_waiting_constraints(cp: CpModel, vars_: CPVariables)

static add_spacial_capacity_constraints(cp: CpModel, vars_: CPVariables, inst: JSSP)

static apply_heuristics(cp: CpModel, solver: CpSolver, vars_: CPVariables) → dict[str, ScheduledAssignment] | None

backup_sol: dict[str, ScheduledAssignment] | None

compute_schedule(inst: JSSP, time_limit: float, offset: float, **kwargs) → tuple[dict[str, ScheduledAssignment] | None, SolutionQuality]

Tries to compute a schedule for the given JSSP instance. Depending on the algorithm there might be no guaranty a solution is found. :param inst: The Problem instance :param time_limit: Maximum computation time(in seconds) the solver is allowed :param offset: Minimum time(in seconds) between call of the function and start time scheduled for any operation :param kwargs: Optional arguments custom to a solver :return: A valid schedule or None

static create_interval_var(lb: int, ub: int, o: Operation, cp: CpModel, ref_time: datetime, optionality: IntVar | None = None) → IntervalVar

Utility method to create and add a new interval variable to the model. Returns the created variable.

static create_model(inst: JSSP, offset: float) → tuple[CpModel, CPVariables]

static create_variables(cp: CpModel, inst: JSSP, offset: int) → CPVariables

static extract_schedule(solver: CpSolver, vars_: CPVariables, inst: JSSP) → dict[str, ScheduledAssignment]

static get_algorithm_info() → AlgorithmInfo

Every algorithm should provide this basic information about itself :return: A typing.NamedTuple containing name, optimality, success guaranty and recommended maximum problem size

static handle_pooling(cp: CpModel, vars_: CPVariables, operations: list[Operation], inst: JSSP)

Creates the part of the model which assigns executing machines to operations which can be executed by different machines.

is_solvable(inst: JSSP) → bool

Checks whether the JSSP instance is solvable at all

static set_objective(cp: CpModel, vars_: CPVariables, inst: JSSP)

static solve_cp(cp: CpModel, vars_: CPVariables, time_limit: float) → tuple[~ortools.sat.python.cp_model.CpSolver, <google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper object at 0x7bd8bf759e80>]

static try_soft_time_constraints(cp: CpModel, vars_, time_limit) → tuple[~ortools.sat.python.cp_model.CpSolver, <google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper object at 0x7bd8bf759e80>]