The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Package {vrpr}

Title: Vehicle Routing Problem Solver Built on 'PyVRP'
Version: 0.1.0
Description: A 'tidyverse'-style interface to high-performance vehicle routing problem (VRP) solving. Vendors the C++ core of the 'PyVRP' solver (https://github.com/PyVRP/PyVRP) and rewires it through 'cpp11', with no 'Python' runtime dependency. Supports the capacitated VRP, time windows, multiple depots, heterogeneous fleets, prize-collecting and multi-trip variants, driven by an iterated local search metaheuristic.
License: MIT + file LICENSE
URL: https://github.com/StrategicProjects/vrpr, https://strategicprojects.github.io/vrpr/
BugReports: https://github.com/StrategicProjects/vrpr/issues
Encoding: UTF-8
Depends: R (≥ 4.3)
Imports: cli, rlang, stats, tibble, vctrs
LinkingTo: cpp11
Suggests: ggplot2, knitr, reticulate, rmarkdown, testthat (≥ 3.0.0), withr
VignetteBuilder: knitr
Config/testthat/edition: 3
SystemRequirements: C++20, GNU make
Config/roxygen2/version: 8.0.0
NeedsCompilation: yes
Packaged: 2026-06-28 13:14:25 UTC; leite
Author: Andre Leite [aut, cre], Marcos Wasilew [aut], Hugo Vasconcelos [aut], Carlos Amorin [aut], Diogo Bezerra [aut], Niels Wouda [ctb, cph] (author of the vendored PyVRP C++ core), Thibaut Vidal [cph] (HGS-CVRP, parts of the vendored C++ core), ORTEC [cph] (HGS-DIMACS, parts of the vendored C++ core)
Maintainer: Andre Leite <leite@castlab.org>
Repository: CRAN
Date/Publication: 2026-07-04 07:10:02 UTC

vrpr: Vehicle Routing Problem Solver Built on 'PyVRP'

Description

logo

A 'tidyverse'-style interface to high-performance vehicle routing problem (VRP) solving. Vendors the C++ core of the 'PyVRP' solver (https://github.com/PyVRP/PyVRP) and rewires it through 'cpp11', with no 'Python' runtime dependency. Supports the capacitated VRP, time windows, multiple depots, heterogeneous fleets, prize-collecting and multi-trip variants, driven by an iterated local search metaheuristic.

Author(s)

Maintainer: Andre Leite leite@castlab.org

Authors:

Other contributors:

See Also

Useful links:

Add a mutually exclusive group of clients

Description

Defines a group from which at most one client is visited (or exactly one if required = TRUE). Useful for prize-collecting with exclusive alternatives (e.g. serving one of several equivalent points). Clients in the group automatically become optional (individual required = FALSE); use prize to encourage a visit.

Usage

add_client_group(model, clients, required = FALSE)

Arguments

model

A vrpr_model.

clients

Vector of client numbers (1-based, in the order of add_clients()) that form the group.

required

If TRUE, exactly one client in the group must be visited; if FALSE (default), at most one.

Value

The updated vrpr_model.

Add clients to the model

Description

Add clients to the model

Usage

add_clients(model, data)

Arguments

model

A vrpr_model.

data

A tibble/data.frame with at least the columns x and y. Optional columns: demand (delivery), pickup, tw_early, tw_late, service, release_time, prize, required. The time-window columns (tw_early/tw_late/service) enable the VRPTW; pickup enables simultaneous pickup and delivery / backhaul.

Value

The updated vrpr_model.

Add a depot to the model

Description

Add a depot to the model

Usage

add_depot(model, x, y, tw_early = 0, tw_late = Inf, service = 0)

Arguments

model

A vrpr_model.

x, y

Depot coordinates.

tw_early, tw_late

Depot time window (opening/closing). tw_late = Inf leaves the closing time unconstrained.

service

Service time at the depot (e.g. loading), per trip.

Value

The updated vrpr_model.

Add a vehicle type to the model

Description

Add a vehicle type to the model

Usage

add_vehicle_type(
  model,
  num_available,
  capacity,
  fixed_cost = 0,
  tw_early = 0,
  tw_late = Inf,
  max_duration = Inf,
  unit_distance_cost = 1,
  unit_duration_cost = 0,
  depot = 1L,
  start_depot = depot,
  end_depot = depot,
  reload_depots = integer(0),
  max_reloads = Inf
)

Arguments

model

A vrpr_model.

num_available

Number of vehicles available of this type.

capacity

Vehicle capacity.

fixed_cost

Fixed cost per vehicle used.

tw_early, tw_late

Vehicle shift time window (start/end). tw_late = Inf leaves the end of the shift unconstrained.

max_duration

Maximum route duration. Inf = unconstrained.

unit_distance_cost, unit_duration_cost

Variable cost per unit of distance and of duration for this type. Varying these (and capacity, fixed_cost) across calls enables a heterogeneous fleet.

depot

Index (1-based) of the depot vehicles of this type start from and return to. Shortcut to set start_depot and end_depot together.

start_depot, end_depot

Indices (1-based) of the start and end depots, in the order of add_depot(). Varying them across types enables the MDVRP (multiple depots).

reload_depots

Indices (1-based) of depots where vehicles of this type may reload/empty mid-route, enabling multi-trip routes. Empty (default) = no reloading.

max_reloads

Maximum number of reloads per route. Inf = unconstrained.

Details

Call add_vehicle_type() several times for a fleet with multiple vehicle types (different capacities, costs, shifts or depots).

Value

The updated vrpr_model.

Cost of a result or solution

Description

Cost of a result or solution

Usage

cost(x, ...)

Arguments

x

A vrp_solve() result or a vrp_solution().

...

Unused.

Value

The objective cost (a numeric scalar); Inf if no feasible solution was found.

ILS solver parameters

Description

ILS solver parameters

Usage

ils_params(
  num_neighbours = 20L,
  min_perturbations = 1L,
  max_perturbations = 25L,
  init_load = 20,
  init_tw = 6,
  init_dist = 6,
  history_length = 300L,
  num_iters_no_improvement = 150000L,
  exhaustive_on_best = TRUE
)

Arguments

num_neighbours

Granular neighbourhood size (k neighbours per client).

min_perturbations, max_perturbations

Range of perturbations per iteration.

init_load, init_tw, init_dist

Initial penalties.

history_length

Length of the late-acceptance history (> 0). Default 300, as in PyVRP.

num_iters_no_improvement

Iterations without improvement before restarting from the best.

exhaustive_on_best

Refine each new best with an exhaustive search?

Value

A list of parameters.

Plot a VRP model (depots and clients only)

Description

Plot a VRP model (depots and clients only)

Usage

## S3 method for class 'vrpr_model'
plot(x, ...)

Arguments

x

A vrp_model().

...

Unused.

Value

A ggplot object.

Plot the solution of a VRP result

Description

Draws depots, clients and the routes (one colour per route) over the instance coordinates. Unvisited optional clients (prize-collecting) appear as hollow circles.

Usage

## S3 method for class 'vrpr_result'
plot(x, show_clients = TRUE, ...)

Arguments

x

A vrp_solve() result.

show_clients

Reserved; clients are always drawn.

...

Unused.

Value

A ggplot object.

Read a VRPTW instance in Solomon format

Description

Reads VRPTW instances in Solomon (and Gehring-Homberger) format, with the VEHICLE section (number and capacity) and the CUSTOMER table (coordinates, demand, time window and service time). Customer 0 is the depot.

Usage

read_solomon(path, num_vehicles = NULL)

Arguments

path

Path to the file.

num_vehicles

Number of vehicles; if NULL, uses the value from the file.

Value

A vrp_model() ready for vrp_solve().

Read an instance in VRPLIB / TSPLIB format

Description

Reads CVRP (and VRPTW) instances in VRPLIB/CVRPLIB format (extended TSPLIB), such as the X set by Uchoa et al. Supports Euclidean coordinates (EDGE_WEIGHT_TYPE : EUC_2D); time-window and service-time sections are read when present.

Usage

read_vrplib(path, num_vehicles = NULL)

Arguments

path

Path to the .vrp file.

num_vehicles

Number of available vehicles. If NULL, uses the VEHICLES/TRUCKS field, the ⁠-k<n>⁠ suffix in the name, or – as a last resort – the number of clients (always feasible).

Value

A vrp_model() ready for vrp_solve().

Routes of a solution, in long (tidy) format

Description

Routes of a solution, in long (tidy) format

Usage

routes(x, ...)

Arguments

x

A vrp_solution().

...

Unused.

Value

A tibble with one row per visit: route_id, depot (start depot, 1-based), position, client, vehicle_type, start_service (start of service) and wait (waiting time). The last two are only meaningful with time windows (VRPTW); depot varies in the MDVRP.

Cost of a solution

Description

Cost of a solution

Usage

solution_cost(solution, cost_evaluator = NULL)

Arguments

solution

A vrp_solution().

cost_evaluator

A vrp_cost_evaluator(). If NULL, uses an evaluator with unit penalties.

Value

The penalised cost (a numeric scalar). For feasible solutions this is the objective cost; the feasible attribute reports feasibility.

One-row summary of a result (tibble)

Description

One-row summary of a result (tibble)

Usage

## S3 method for class 'vrpr_result'
summary(object, ...)

Arguments

object

A vrp_solve() result.

...

Unused.

Value

A one-row tibble with cost, feasibility, number of routes, iterations and runtime.

Unvisited optional clients

Description

In prize-collecting problems, clients with required = FALSE may be left out if the prize does not offset the routing cost.

Usage

unvisited_clients(x, ...)

Arguments

x

A vrp_solve() result.

...

Unused.

Value

An integer vector of the (1-based) client numbers not visited.

Cost evaluator (CostEvaluator)

Description

Creates a penalised-cost evaluator. Penalties multiply constraint violations (load, time window, maximum distance) to form the smoothed cost the solver minimises. For a feasible solution, the penalised cost equals the objective cost.

Usage

vrp_cost_evaluator(load_penalties = 1, tw_penalty = 1, dist_penalty = 1)

Arguments

load_penalties

Penalty per unit of excess load, per load dimension. A scalar is recycled across all dimensions.

tw_penalty

Penalty per unit of time warp (time-window violation).

dist_penalty

Penalty per unit of distance above the maximum.

Value

A vrpr_cost_evaluator object.

Build a vehicle routing (VRP) model

Description

vrp_model() creates an empty model to which depots, clients and vehicle types are added via the pipe (⁠|>⁠). It is the tidy equivalent of PyVRP's Model class – the data boundary uses tibbles, not one object at a time.

Usage

vrp_model()

Value

A vrpr_model object.

Examples

clients <- tibble::tibble(
  x = c(10, 25, 40), y = c(5, 30, 12),
  demand = c(10, 15, 8)
)
m <- vrp_model() |>
  add_depot(x = 0, y = 0) |>
  add_clients(clients) |>
  add_vehicle_type(num_available = 5, capacity = 100)
m

Assemble the problem data (ProblemData) from a model

Description

Builds PyVRP's C++ ProblemData structure from a vrp_model(). Locations follow PyVRP's convention: depots first (low indices), then clients.

Usage

vrp_problem_data(model, distance = NULL, duration = NULL)

Arguments

model

A vrp_model() with at least one depot and one vehicle type.

distance, duration

Matrices (numeric, ⁠n x n⁠, locations in depots-then-clients order) of distance and duration. If NULL, they are computed as the rounded Euclidean distance between coordinates; duration defaults to distance.

Details

Integer measures (distance, duration, cost, load) travel as R numeric with integer semantics; non-integer values are rejected at the C++ boundary. Use Inf for "unconstrained" limits (e.g. tw_late).

Value

A vrpr_problem_data object (a wrapper around a C++ external pointer).

Generate a random solution

Description

Generate a random solution

Usage

vrp_random_solution(problem_data, seed = 42L)

Arguments

problem_data

A vrp_problem_data().

seed

Integer seed.

Value

A vrpr_solution object.

Build a solution from explicit routes

Description

Build a solution from explicit routes

Usage

vrp_solution(problem_data, routes)

Arguments

problem_data

A vrp_problem_data().

routes

A list of integer vectors; each vector is a route given as client numbers (1..n_clients), in visit order. All routes use the first vehicle type.

Value

A vrpr_solution object.

Solve a VRP model

Description

Runs the iterated local search (ILS) solver on a model, using PyVRP's vendored C++ core.

Usage

vrp_solve(model, stop, seed = 42L, params = ils_params(), display = TRUE)

Arguments

model

A vrp_model() or an already-assembled vrp_problem_data().

stop

A stopping criterion (see vrpr_stop), e.g. max_runtime(10).

seed

Integer seed for reproducibility.

params

Solver parameters (see ils_params()).

display

Show progress via {cli}?

Value

A vrpr_result object with the best solution, cost, routes and run statistics. Use cost(), routes() and summary() to inspect it.

Examples

clients <- tibble::tibble(
  x = c(10, 25, 40, 15), y = c(5, 30, 12, 22),
  demand = c(10, 15, 8, 12)
)
res <- vrp_model() |>
  add_depot(x = 0, y = 0) |>
  add_clients(clients) |>
  add_vehicle_type(num_available = 3, capacity = 50) |>
  vrp_solve(stop = max_iterations(200), display = FALSE)

cost(res)
routes(res)

Solver stopping criteria

Description

Control when the iterated local search loop should terminate. Each function returns a callable object (closure) that the solver invokes every iteration, receiving the cost of the current best solution and returning TRUE to stop.

Usage

max_runtime(seconds)

max_iterations(max_iters)

no_improvement(n)

first_feasible()

Arguments

seconds

Maximum run time, in seconds.

max_iters

Maximum number of iterations.

n

Number of consecutive iterations without improvement before stopping.

Details

These are the R equivalent of PyVRP's pyvrp.stop module.

Value

An object of class vrpr_stop: a function ⁠function(best_cost, feasible)⁠ returning TRUE/FALSE.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.