discrete_optimization.workforce.commons package

Submodules

discrete_optimization.workforce.commons.fairness_modeling module

class discrete_optimization.workforce.commons.fairness_modeling.ModelisationDispersion(value)

Bases: Enum

An enumeration.

EXACT_MODELING_DUPLICATED_VARS = 1

EXACT_MODELING_WITH_IMPLICATION = 0

MAX_DIFF = 2

PROXY_MAX_MIN = 3

PROXY_MIN_MAX = 4

PROXY_SLACK = 6

PROXY_SUM = 5

discrete_optimization.workforce.commons.fairness_modeling_cpmpy module

discrete_optimization.workforce.commons.fairness_modeling_cpmpy.cumulate_value_per_teams(used_team: dict[int, Expression] | NDVarArray, allocation_variables: NDVarArray | list[dict[int, Expression]], value_per_task: list[int], number_teams: int | None = None, name_value: str | None = '')

• used_team :

  • either a dict : {index_team: boolean_var} for index_team in range(nb_teams)

  • or simply an array of size (nb_teams)

• allocation_variables :

  • either a 2D array of vars : [index_activity, index_team] or a list of size (nb_activities) of dict : {index_team: boolean} for index_team in “available_teams for a given task”

• value per task :

  • list of values linked to task, to aggregate for a given team.

discrete_optimization.workforce.commons.fairness_modeling_cpmpy.cumulate_value_per_teams_version_2(used_team: dict[int, Expression] | NDVarArray, allocation_variables: NDVarArray | list[dict[int, Expression]], value_per_task: list[int], number_teams: int | None = None, name_value: str | None = '')

• used_team :

  • either a dict : {index_team: boolean_var} for index_team in range(nb_teams)

  • or simply an array of size (nb_teams)

• allocation_variables :

  • either a 2D array of vars : [index_activity, index_team] or a list of size (nb_activities) of dict : {index_team: boolean} for index_team in “available_teams for a given task”

• value per task :

  • list of values linked to task, to aggregate for a given team.

discrete_optimization.workforce.commons.fairness_modeling_cpmpy.define_fairness_criteria_from_cumulated_value(used_team: dict[int, Expression] | NDVarArray, cumulated_value_per_team: NDVarArray, value_per_task: list[int], modelisation_dispersion: ModelisationDispersion, cumulated_value_per_team_nz: NDVarArray | None = None, name_value: str | None = '')

discrete_optimization.workforce.commons.fairness_modeling_cpmpy.model_fairness(used_team: dict[int, Expression] | NDVarArray, allocation_variables: NDVarArray | list[dict[int, Expression]], value_per_task: list[int], modelisation_dispersion: ModelisationDispersion, number_teams: int | None = None, name_value: str | None = '')

discrete_optimization.workforce.commons.fairness_modeling_ortools module

discrete_optimization.workforce.commons.fairness_modeling_ortools.cumulate_value_per_teams(used_team: dict[int, IntVar] | list[IntVar], allocation_variables: list[list[IntVar]] | list[dict[int, IntVar]], value_per_task: list[int], cp_model: CpModel, number_teams: int | None = None, name_value: str | None = '')

• used_team :

  • either a dict : {index_team: boolean_var} for index_team in range(nb_teams)

  • or simply an array of size (nb_teams)

• allocation_variables :

  • either a 2D array of vars : [index_activity, index_team] or a list of size (nb_activities) of dict : {index_team: boolean} for index_team in “available_teams for a given task”

• value per task :

  • list of values linked to task, to aggregate for a given team.

discrete_optimization.workforce.commons.fairness_modeling_ortools.cumulate_value_per_teams_version_2(used_team: dict[int, IntVar] | list[IntVar], allocation_variables: list[list[IntVar]] | list[dict[int, IntVar]], value_per_task: list[int], cp_model: CpModel, number_teams: int | None = None, name_value: str | None = '')

• used_team :

  • either a dict : {index_team: boolean_var} for index_team in range(nb_teams)

  • or simply an array of size (nb_teams)

• allocation_variables :

  • either a 2D array of vars : [index_activity, index_team] or a list of size (nb_activities) of dict : {index_team: boolean} for index_team in “available_teams for a given task”

• value per task :

  • list of values linked to task, to aggregate for a given team.

discrete_optimization.workforce.commons.fairness_modeling_ortools.define_fairness_criteria_from_cumulated_value(used_team: dict[int, IntVar] | list[IntVar], cumulated_value_per_team: list[IntVar], value_per_task: list[int], modelisation_dispersion: ModelisationDispersion, cp_model: CpModel, cumulated_value_per_team_nz: list[IntVar] | None = None, name_value: str | None = '')

discrete_optimization.workforce.commons.fairness_modeling_ortools.model_fairness(used_team: dict[int, IntVar] | list[IntVar], allocation_variables: list[list[IntVar]] | list[dict[int, IntVar]], value_per_task: list[int], modelisation_dispersion: ModelisationDispersion, cp_model: CpModel, number_teams: int | None = None, name_value: str | None = '')

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