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https://github.com/loggi/loggibud

Real-world benchmarks for urban delivery problems, including vehicle routing and facility location problems.
https://github.com/loggi/loggibud

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Real-world benchmarks for urban delivery problems, including vehicle routing and facility location problems.

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README 

          
Loggi Benchmark for Urban Deliveries (BUD) 

==================



The Loggi Benchmark for Urban Deliveries contains datasets and benchmark

scripts for large-scale problems using as a testbed some of Brazil's largest

cities. The data is synthesized completely from public sources and

representative of the challenges we face on real world deliveries for the

problems outlined in the repository.



To get started with the development using LoggiBUD, check out our 

[quickstart tutorial](./docs/quickstart.md). If you want to learn how to solve

large-scale delivery problems, check our [Introductory course 

(in Portuguese)](./docs/loggibud_univesp.md) developed in partnership with

The Virtual University of the State of São Paulo (Univesp).



# Dataset



The dataset simulates the challenge of a large delivery company in the

last-mile step of the supply chain. Worldwide, metropolitan areas like Rio de

Janeiro witness the delivery of tens or even hundreds of thousands of parcels

and mail.  Deliveries on those areas are unexpectedly very unevenly

distributed. Rich and densily populated areas drive most of the deliveries.

Besides economic factors, our data also do consider the geography of the

represented locations and real street travel distances, as the reality of

complex cities is not well modelled by euclidian distances.



On our first version, We provide 90 train instances and 30 evaluation instances

for the end-to-end problem on Rio de Janeiro (RJ), Brasília (DF) and Belém (PA).

The instance sizes range from 7k to 32k deliveries. The challenge is to position

consolidation hubs across the city and split demands into delivery vehicles. 



  



While our goal is to solve the problem on an end-to-end manner, we also provide

a baseline distribution of demands into 11 hubs obtained using a p-hub

assignment. This leads to 990 train instances and 330 evaluation instances on

the CVRP challenge.



     



# Tasks



### Task 1 - Last-Mile Capacitated Vehicle Routing Problem



This first task is a classic Capacitated Vehicle Routing Problem (CVRP) with

minimizing travel distance as objetive. Although solving such large CVRPs with

complete information is often impractical, these solutions are good targets for

Task 2.



### Task 2 - Last-Mile Incremental Capacitated Vehicle Routing Problem



This is considered a particular case of Stochastic and Dynamic Capacitated

Vehicle Routing Problems (SD-CVRP). The objetive is to sort deliveries into

routes one by one as they arrive. You may use only historical information and

the previously routed deliveries from the same instance. No recombination is

allowed after assignment.



From an operations point of view, this solution class is a lot more powerful as

it doesn't require full information before start routing. Instead, hub

operators can route deliveries as they arrive.



### Task 3 - End-to-end last-mile problem



This is an extension of the task 1.2, but without fixed dispatch locations.

Therefore, locations and allocation of requests to dispatch locations is

considered part of the solution, but should be the same accross multiple

instances. This can be considered an end-to-end optimization of last-mile

delivery operations.



# Motivation



This benchmark is an effort to make new operations research solutions closer to

real-world applications faced by delivery companies. We believe this work can

help both practitioners and academics to reduce the gap between

state-of-the-art and practice.



We identify that several promising solutions in academic literature have

evaluation issues. Several papers only include experiments for small or

unrealistic problems. We also expected real-world instances to help researchers

to come up with new ideas for solving large routing and location problems.



### Why a new dataset



There are several famous datasets for the Vehicle Routing Problem (VRP).

However, there are limitations to these instances that make them hard to apply

to real-world last-mile problems. Some of them include:



* Small instances

* Ignore streets, use only euclidean distances

* No discussion on aggregation levels



Most instances also fail to model real challenges of dynamic and stochastic

instances, where the stochastic information is usually presented as

probabilities on deterministic points. We instead present stochastic

information as historical deliveries performed under the same distribution.



### Why a GitHub benchmark



We want to make publishing results as easy as opening a pull-request. We also

want to provide you with code to evaluate your solution and make sure it's

ready for publication. If you're not familiar with making open-source

contributions, don't worry. Open an issue, and we'll be happy to guide you with

your submission.



# How to submit



To include your method to the benchmark leaderboards, you must submit the

solution data into a pull request.



While submitting the code is not required, we strongly suggest making your

research code available to make reproducing the results more accessible. You

can publish the code on your repository and include a link on your submission.

We also have some baselines on this repository. If you want to include your

code as a baseline, we'll be happy to revise it.



Don't forget to acknowledge the literature and code you have used on your

solution.



# Instance generation pipeline



If you want more details on how we generate our instances and how they relate

to actual deliveries, please check our [generation pipeline

documentation](./loggibud/v1/instance_generation/). It also includes the steps for reproducing the

provided instances.



# Citing this work



If you use this data in academic work, please cite our repository. A full paper

describing it should be available soon.



```@misc{Charles2013,

  author = {Loggi},

  title = {loggiBUD: Loggi Benchmark for Urban Deliveries},

  year = {2021},

  publisher = {GitHub},

  journal = {GitHub repository},

  howpublished = {\url{https://github.com/loggi/loggibud}}

}

```



# Disclaimer



This repository is not an official Loggi product. Use it at your own risk under

the terms of the MIT license.