Vehicle routing and scheduling problems
Donated on 10/6/2019
Data collection was conducted through notes taken during the distribution of orders in a courier company that operates in the region and in the city of São Paulo (Brazil).
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Clustering
Feature Type
Integer, Real
# Instances
18
# Features
9
Dataset Information
Additional Information
The attributes are the number of crew members, form of cargo stowage/transshipment (manual or mechanized/palletized), service difficulty (waiting time, identification of delivery person, etc.), distance from the depot in kilometers, average monthly cargo, average daily per point, average number of volumes transported per day, average vehicle occupation (%), and the type of vehicle used. The data set (Vehicle routing and scheduling problems) was used in academic research at the Universidade Nove de Julho - Postgraduate Program in Informatics and Knowledge Management.
Has Missing Values?
No
Variable Information
1. Number of crew members 2. Form of cargo stowage/transshipment (manual = 1 or mechanized/palletized = 2) 3. Service difficulty (waiting time, identification of delivery person, etc.) - (low service difficulty = 1; 2, 3 and 4 intermediate; high service difficulty = 5) 4. Distance from the depot in kilometers 5. Average monthly cargo 6. Average daily per point 7. Average number of volumes transported per day (box) 8. Average vehicle occupation (%) 9. Type of vehicle (Up to 0.7 tons of capacity = 1; Up to 1.5 tons of capacity; Up to 3 tons of capacity).
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset vehicle_routing_and_scheduling_problems = fetch_ucirepo(id=546) # data (as pandas dataframes) X = vehicle_routing_and_scheduling_problems.data.features y = vehicle_routing_and_scheduling_problems.data.targets # metadata print(vehicle_routing_and_scheduling_problems.metadata) # variable information print(vehicle_routing_and_scheduling_problems.variables)
Vehicle routing and scheduling problems. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C5VC9V.
@misc{misc_vehicle_routing_and_scheduling_problems_546,
title = {{Vehicle routing and scheduling problems}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5VC9V}
}DOI
Notes
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.