labscheduler.solver_interface module

Interface of JSSP solver

class labscheduler.solver_interface.AlgorithmInfo(name: str, is_optimal: bool, success_guaranty: bool, max_problem_size: int)

Bases: NamedTuple

Every algorithm should provide this basic information

_asdict()

Return a new dict which maps field names to their values.

_field_defaults = {}

_fields = ('name', 'is_optimal', 'success_guaranty', 'max_problem_size')

classmethod _make(iterable)

Make a new AlgorithmInfo object from a sequence or iterable

_replace(**kwds)

Return a new AlgorithmInfo object replacing specified fields with new values

is_optimal: bool

Alias for field number 1

max_problem_size: int

Alias for field number 3

name: str

Alias for field number 0

success_guaranty: bool

Alias for field number 2

class labscheduler.solver_interface.JSSPSolver

Bases: ABC

The interface of a JSSP solver used in the LabScheduler

_abc_impl = <_abc._abc_data object>

abstractmethod compute_schedule(inst: JSSP, time_limit: float, offset: float, **kwargs) → tuple[Schedule | 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

abstractmethod 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

abstractmethod is_solvable(inst: JSSP) → bool

Checks whether the JSSP instance is solvable at all