pyepo.model.grb.vrp¶

Capacitated vehicle routing problem with binding-constraint tracking for CaVE

Classes¶

`vrpABModel`	Abstract Gurobi-backed model for the capacitated vehicle routing problem.
`vrpRCIModel`	CVRP formulation with 2-degree constraints and lazy rounded-capacity cuts.
`vrpMTZModel`	CVRP formulation on a directed graph with MTZ-style capacity constraints
`vrpMTZModelRel`	LP relaxation of `vrpMTZModel`.

Module Contents¶

class pyepo.model.grb.vrp.vrpABModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)¶

Bases: pyepo.model.grb.grbmodel.optGrbModel

Abstract Gurobi-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.

Variables:

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

num_nodes¶

nodes¶

edges¶

demands¶

capacity¶

num_vehicle¶

property num_cost: int¶: number of costs to be predicted

getTour(sol: numpy.ndarray | torch.Tensor | list) → list[list[int]]¶

Reconstruct vehicle tours from an undirected edge-selection vector.

Parameters:: 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

copy() → Self¶: A method to copy the model

class pyepo.model.grb.vrp.vrpRCIModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)¶

Bases: 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; the cuts added at the optimum are tracked on model._lazy_constrs for downstream CaVE cone extraction.

setObj(c: numpy.ndarray | torch.Tensor | list) → None¶

A method to set the objective function

Parameters:: c – cost vector aligned with self.edges

solve() → tuple[numpy.ndarray, float]¶

A method to solve the model

Returns:: edge-selection vector (uint8) and objective value (float)
Return type:: tuple

class pyepo.model.grb.vrp.vrpMTZModel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)¶

Bases: 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].

setObj(c: numpy.ndarray | torch.Tensor | list) → None¶

A method to set the objective function

Parameters:: c – cost vector aligned with self.edges (one cost per undirected edge)

solve() → tuple[numpy.ndarray, float]¶

A method to solve the model

Returns:: edge-selection vector (uint8) and objective value (float)
Return type:: tuple

relax() → vrpMTZModelRel¶: A method to get linear relaxation model

class pyepo.model.grb.vrp.vrpMTZModelRel(num_nodes: int, demands: list[float] | numpy.ndarray, capacity: float, num_vehicle: int)¶

Bases: vrpMTZModel

LP relaxation of vrpMTZModel.

solve() → tuple[numpy.ndarray, float]¶: A method to solve the model — returns fractional edge selections

relax() → NoReturn¶: A forbidden method to relax MIP model

getTour(sol: numpy.ndarray | torch.Tensor | list) → list[list[int]]¶: A forbidden method to get a tour from solution