pyepo.model.omo.vrp

Capacitated vehicle routing problem

Classes

vrpABModel	Abstract Pyomo-backed model for the capacitated vehicle routing problem.
vrpMTZModel	CVRP formulation on a directed graph with MTZ-style capacity constraints.
vrpMTZModelRel	LP relaxation of vrpMTZModel.

Module Contents

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

Bases: pyepo.model.bases.vrpABBase, 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.

Variables:

solver – optimization solver in the background

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

Bases: 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].

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

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