Benchmark Instance Generation¶

Benchmark instance generation settings are defined in file benchmark_instances_generator.py.

Data files format¶

************************
* TOPTW instances      *
************************

The first line contains the following data:

    k v N t

Where
    k = not relevant
    v = with this number of paths, all vertices can be visited
    N = number of vertices
    t = not relevant


The next line contains the following data:

        D Q

Where
    D = not relevant (in many files this number is missing)
    Q = not relevant


The remaining lines contain the data of each point.
For each point, the line contains the following data:

    i x y d S f a list O C

Where
    i = vertex number
    x = x coordinate
    y = y coordinate
    d = service duration or visiting time
    S = profit of the location
    f = not relevant
    a = not relevant
    list = not relevant (length of the list depends on a)
        O = opening of time window (earliest time for start of service)
    C = closing of time window (latest time for start of service)

* REMARKS *
    - The first point (index 0) is the starting AND ending point.
    - The number of paths (P) is not included in the data file. This value can vary (1,2,3, etc.).
    - The time budget per path (Tmax) equals the closing time of the starting point.
    - The Euclidean distance is used and rounded down to the first decimal for the Solomon instances
        and to the second decimal for the instances of Cordeau et al.

Taken from: https://www.mech.kuleuven.be/en/cib/op#autotoc-item-autotoc-6

BenchmarkInstanceGenerator¶

class maenvs4vrp.environments.toptw.benchmark_instances_generator.BenchmarkInstanceGenerator(instance_type: str = 'Solomon', set_of_instances: set = None, device: str | None = 'cpu', batch_size: Size | None = None, seed: int = None)[source]¶

TOPTW benchmark instance generation class.

__init__(instance_type: str = 'Solomon', set_of_instances: set = None, device: str | None = 'cpu', batch_size: Size | None = None, seed: int = None) → None[source]¶

Constructor. Create an instance space of one or several sets of data.

Parameters:

instance_type (str) – Instance type. Can be “Solomon” or “Homberger”. Defaults to “Solomon”.
set_of_instances (set) – Set of instances file names. Defaults to None.
device (str, optional) – Type of processing. It can be “cpu” or “gpu”. Defaults to “cpu”.
batch_size (torch.Size, optional) – Batch size. If not specified, defaults to 1.
seed (int) – Random number generator seed. Defaults to None.

Returns:

None.

eliminate_extra_columns(instance_data: list) → list[source]¶

Cordeau instances have extra columns in some rows. This function eliminates the extra columns. This will also correct position of total time in row 0 for all instances.

Parameters:: instance_data (list) – Instance data.
Returns:: List with eliminated rows.
Return type:: list

get_instance(instance_name: str, num_agents: int = None) → Dict[source]¶

Get an instance with custom number of agents.

Parameters:

instance_name (str) – Instance file name.
num_agents (int) – Number of agents. Defaults to None.

Returns:

Instance data.

Return type:

Dict

classmethod get_list_of_benchmark_instances()[source]¶

Get list of possible instances from benchmark files.

Parameters:: n/a.
Returns:: None.

load_set_of_instances(set_of_instances: set = None)[source]¶

Load every instance on set_of_instances set.

Parameters:: set_of_instances (set) – Set of instances file names. Defaults to None.
Returns:: None.

parse_instance_data(instance_data: list, instance_name: str) → Dict[source]¶

Parse instance data list into a dictionary.

Parameters:: instance_data (list) – Instance data.
Returns:: Parsed instance data.
Return type:: Dict

random_sample_instance(instance_name: str = None, num_agents: int = None, num_nodes: int = None, seed: int = None, device: str | None = 'cpu') → Dict[source]¶

Sample one instance from instance space, randomly adjusting the nodes.

Parameters:

instance_name (str) – Instance file name. Defaults to None.
num_agents (int) – Total number of agents. Defaults to None.
num_nodes (int) – Total number of nodes. Defaults to None.
seed (int) – Random number generator seed. Defaults to None.

Returns:

Instance data.

Return type:

Dict

read_instance_data(instance_name: str) → Dict[source]¶

Read instance data from file.

Parameters:: instance_name (str) – Instance file name.
Returns:: Instance data.
Return type:: Dict

sample_instance(num_agents: int = None, num_nodes: int = None, service_times: float = None, profits: str = None, instance_name: str = None, sample_type: str = 'random', batch_size: Size | None = None, n_augment: int | None = None, seed: int = None, device: str | None = 'cpu') → Dict[source]¶

Sample one instance from instance space.

Parameters:

num_agents (int) – Total number of agents. Defaults to None.
num_nodes (int) – Total number of nodes. Defaults to None.
capacity (int) – Capacity of the agents. Defaults to None.
service_times (float) – Service time in the nodes. Defaults to None.
instance_name (str) – Instance name. Defaults to None.
sample_type (str) – Sample type. It can be “random” or something else for “first n”. Defaults to “random”.
batch_size (torch.Size or None) – Batch size. Defaults to None.
n_augment (int, optional) – Data augmentation. Defaults to None.
seed (int) – Random number generator seed. Defaults to None.

Returns:

Instance data.

Return type:

Dict

sample_name_from_set(seed: int = None) → str[source]¶

Sample one instance from instance set.

Parameters:: seed (int) – Random number generator seed. Defaults to None.
Returns:: Instance sample name.
Return type:: str