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Assemble the problem data (ProblemData) from a model

Source: R/problem_data.R
vrp_problem_data.Rd

Builds PyVRP's C++ ProblemData structure from a vrp_model(). Locations follow vrpr's convention: depots first (low indices), then clients, then one pickup and one delivery location per shipment.

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

Optional n x n numeric matrices of travel distance and travel duration between all n locations, e.g. from a road network or a routing engine (OSRM, osmnx, ...). Rows and columns follow vrpr's location order: depots (in the order they were added), then clients, then one pickup and one delivery location per shipment. Asymmetric matrices are fine. Entries must be non-negative integers (PyVRP uses integer measures), so round to the unit you want (metres, seconds) first. If distance is NULL, it is computed as the rounded Euclidean distance between the coordinates (the TSPLIB EUC_2D convention). If duration is NULL, it is taken equal to distance; a warning is issued when that happens with an explicit distance and the model has temporal features (time windows, service times, maximum duration, ...), because those are then measured in distance units. With explicit matrices the coordinates in add_depot()/add_clients() are only used by plot().

Value

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

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).

Examples

# A triangle depot/A/B with an asymmetric road-network matrix (metres) and
# a separate travel-time matrix (seconds).
clients <- tibble::tibble(x = c(1, 2), y = c(0, 1), demand = c(5, 5))
dist <- matrix(c(0, 1200, 1500,
                 1300, 0, 800,
                 1500, 900, 0), 3, byrow = TRUE)
time <- matrix(c(0, 180, 240,
                 200, 0, 120,
                 240, 130, 0), 3, byrow = TRUE)
m <- vrp_model() |>
  add_depot(0, 0) |>
  add_clients(clients) |>
  add_vehicle_type(num_available = 1, capacity = 10)
pd <- vrp_problem_data(m, distance = dist, duration = time)
pd
#> 
#> ── ProblemData ─────────────────────────────────────────────────────────────────
#> • 2 clients - 1 depot (3 locations)
#> • 1 vehicle type - 1 vehicle
#> • 1 load dimension - 1 profile
#> • time windows: no