discrete_optimization.top package
Subpackages
Submodules
discrete_optimization.top.parser module
- discrete_optimization.top.parser.get_data_available(data_folder: str | None = None, data_home: str | None = None) list[str][source]
Get datasets available for tsp.
- Params:
- data_folder: folder where datasets for weighted tardiness problem should be found.
If None, we look in “wt” subdirectory of data_home.
- data_home: root directory for all datasets. Is None, set by
default to “~/discrete_optimization_data “
- discrete_optimization.top.parser.parse_file(file_path: str) TeamOrienteeringProblem2D[source]
discrete_optimization.top.problem module
- class discrete_optimization.top.problem.CustomerTop(name: str | int, reward: float)[source]
Bases:
BasicCustomer
- class discrete_optimization.top.problem.CustomerTop2D(name: str | int, reward: float, x: float, y: float)[source]
Bases:
CustomerTop
- class discrete_optimization.top.problem.TeamOrienteeringProblem(vehicle_count: int, max_length_tours: float, customer_count: int, customers: Sequence[BasicCustomer], start_indexes: list[int], end_indexes: list[int])[source]
Bases:
VrpProblem- count_multiple_visits(vrp_sol: VrpSolution) bool[source]
- customers: list[CustomerTop]
- evaluate(solution: VrpSolution) dict[str, float][source]
Evaluate a given solution object for the given problem.
This method should return a dictionnary of KPI, that can be then used for mono or multiobjective optimization.
- Parameters:
variable (Solution) – the Solution object to evaluate.
Returns: dictionnary of float kpi for the solution.
- evaluate_function(vrp_sol: VrpSolution) tuple[list[list[float]], list[float], float, list[float]][source]
- get_objective_register() ObjectiveRegister[source]
Returns the objective definition.
Returns (ObjectiveRegister): object defining the objective criteria.
- max_length_tours: float
- satisfy(variable: VrpSolution) bool[source]
Computes if a solution satisfies or not the constraints of the problem.
- Parameters:
variable – the Solution object to check satisfability
Returns (bool): boolean true if the constraints are fulfilled, false elsewhere.
- class discrete_optimization.top.problem.TeamOrienteeringProblem2D(vehicle_count: int, max_length_tours: float, customer_count: int, customers: Sequence[BasicCustomer], start_indexes: list[int], end_indexes: list[int])[source]
Bases:
TeamOrienteeringProblem- customers: list[CustomerTop]
- max_length_tours: float