pyepo.model.omo.vrp
===================

.. py:module:: pyepo.model.omo.vrp

.. autoapi-nested-parse::

   Capacitated vehicle routing problem



Classes
-------

.. autoapisummary::

   pyepo.model.omo.vrp.vrpABModel
   pyepo.model.omo.vrp.vrpMTZModel
   pyepo.model.omo.vrp.vrpMTZModelRel


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

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

   Bases: :py:obj:`pyepo.model.omo.omomodel.optOmoModel`


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

   Pyomo lacks easy callback support, so no lazy-cut RCI formulation exists
   for this backend — only MTZ. 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
   :ivar solver: optimization solver in the background



   .. 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:: vrpMTZModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int, solver: str = 'glpk')

   Bases: :py:obj:`vrpABModel`


   CVRP formulation on a directed graph with MTZ-style capacity constraints.
   Cost vector is per undirected edge: cost ``c[k]`` is assigned to both
   ``x[i,j]`` and ``x[j,i]``.


   .. 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, solver: str = 'glpk')

   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