pyepo.model.copt.vrp
====================

.. py:module:: pyepo.model.copt.vrp

.. autoapi-nested-parse::

   Capacitated vehicle routing problem



Classes
-------

.. autoapisummary::

   pyepo.model.copt.vrp.vrpABModel
   pyepo.model.copt.vrp.vrpRCIModel
   pyepo.model.copt.vrp.vrpMTZModel
   pyepo.model.copt.vrp.vrpMTZModelRel


Module Contents
---------------

.. py:class:: vrpABModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)

   Bases: :py:obj:`pyepo.model.copt.coptmodel.optCoptModel`


   Abstract COPT-backed model for the capacitated vehicle routing problem.

   A single-customer route is excluded so all edge variables stay strictly
   binary; if a single-stop route is actually needed, duplicate the depot.

   :ivar num_nodes: number of nodes (including depot at index 0)
   :ivar nodes: list of node indices
   :ivar edges: undirected edges as (i, j) with i < j
   :ivar demands: customer demands (excluding depot)
   :ivar capacity: per-vehicle capacity
   :ivar num_vehicle: number of vehicles



   .. py:attribute:: num_nodes


   .. py:attribute:: nodes


   .. py:attribute:: edges


   .. py:attribute:: demands


   .. py:attribute:: capacity


   .. py:attribute:: num_vehicle


   .. py:property:: num_cost
      :type: int


      number of costs to be predicted


   .. py:method:: getTour(sol: numpy.ndarray | torch.Tensor | list) -> list[list[int]]

      Reconstruct vehicle tours from an undirected edge-selection vector.

      :param sol: per-edge selection values aligned with ``self.edges``

      :returns: list of tours; each tour is a node sequence starting and ending at the depot



   .. py:method:: copy() -> Self

      A method to copy the model



.. py:class:: vrpRCIModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)

   Bases: :py:obj:`vrpABModel`


   CVRP formulation with 2-degree constraints and lazy rounded-capacity cuts.

   Uses one undirected Var per edge (``x[j,i]`` aliases ``x[i,j]``). Subtour
   elimination and rounded capacity inequalities are added lazily during
   branch-and-cut via a COPT callback.


   .. py:method:: setObj(c: numpy.ndarray | torch.Tensor | list) -> None

      A method to set the objective function

      :param c: cost vector aligned with ``self.edges``



   .. py:method:: solve() -> tuple[numpy.ndarray, float]

      A method to solve the model

      :returns: edge-selection vector (uint8) and objective value (float)
      :rtype: tuple



.. py:class:: vrpMTZModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)

   Bases: :py:obj:`vrpABModel`


   CVRP formulation on a directed graph with MTZ-style capacity constraints
   (no lazy cuts). Cost vector is per undirected edge: cost ``c[k]`` is
   assigned to both ``x[i,j]`` and ``x[j,i]``.


   .. py:method:: setObj(c: numpy.ndarray | torch.Tensor | list) -> None

      A method to set the objective function

      :param c: cost vector aligned with ``self.edges`` (one cost per undirected edge)



   .. py:method:: solve() -> tuple[numpy.ndarray, float]

      A method to solve the model

      :returns: edge-selection vector (uint8) and objective value (float)
      :rtype: tuple



   .. py:method:: relax() -> vrpMTZModelRel

      A method to get linear relaxation model



.. py:class:: vrpMTZModelRel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)

   Bases: :py:obj:`vrpMTZModel`


   LP relaxation of :class:`vrpMTZModel`.


   .. py:method:: solve() -> tuple[numpy.ndarray, float]

      A method to solve the model — returns fractional edge selections



   .. py:method:: relax() -> NoReturn

      A forbidden method to relax MIP model



   .. py:method:: getTour(sol: numpy.ndarray | torch.Tensor | list) -> list[list[int]]

      A forbidden method to get a tour from solution