https://github.com/melvidoni/rsppfp
Shortest Path Problem with Forbidden Paths in R
https://github.com/melvidoni/rsppfp
graph-algorithms graph-theory r rpackage shortest-path-problem
Last synced: 8 months ago
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Shortest Path Problem with Forbidden Paths in R
- Host: GitHub
- URL: https://github.com/melvidoni/rsppfp
- Owner: melvidoni
- Created: 2018-05-17T13:26:59.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2019-10-02T03:33:58.000Z (over 6 years ago)
- Last Synced: 2024-11-07T04:49:04.911Z (over 1 year ago)
- Topics: graph-algorithms, graph-theory, r, rpackage, shortest-path-problem
- Language: R
- Homepage: https://melvidoni.github.io/rsppfp/
- Size: 913 KB
- Stars: 4
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
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README
# rsppfp 
[](https://cran.r-project.org/package=rsppfp)
[](https://cranlogs.r-pkg.org/badges/grand-total/rsppfp?color=yellow)
[](https://travis-ci.org/melvidoni/rsppfp)
[](https://codecov.io/github/melvidoni/rsppfp?branch=master)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://doi.org/10.5281/zenodo.1412539)
The **rsppfp** package implements different algorithms for transforming graphs in the _Shortest Path Problem with Forbidden Paths_ (SPPFP). This problem is an important concept in the field of graph theories, and it is a variant of the traditional _shortest path problem_. In here, there is an additional constrait that takes the form of a finite set of forbidden paths (arc sequences of at least three nodes) that cannot be part of any solution path.
This problem is solved by transforming the original graph `G` and its set of forbidden paths `F`, generating a new graph `G*` in which traditional _shortest path_ algorithms can be applied, and obtain solutions that abide to the restrictions. This approach has a number of advantages:
- It allows solving the original _shortest path problem_ in `G*` with algorithms that efficiently manage time and processing resources constraints, having been implemented in a plethora of languages.
- The resulting `G*` is highly compatible with existing libraries, and can be used as input data for other, more complex problems and researches.
- In many cases -i.e. logistics- `G` and `F` remain unchanged for long periods of time. Thus, the transformation is completed only once, and `G*` can be stored along with the original graph. A new conversion is required only on the rare cases where the graph, or its forbidden paths, are modified.
- The input data is provided as common data frames, increasing the versatility of this package.
This solving process is illustrated in Figure 1, using a paper notation to indicate input and output data. Even more, rsppfp scope and key functionalities are also highlighted.
```{r out.width = '100%', fig.align="center", fig.cap="Figure 1. Flow diagram showcasing an SPPFP solving process, and highlighting rsppfp package's contribution.", echo=FALSE}
knitr::include_graphics("man/figures/fig1.png",
auto_pdf = getOption("knitr.graphics.auto_pdf", FALSE),
dpi = NULL)
```
## Algorithms
rsppfp implements two different algorithms, each one suited for different situations:
1. Villeneuve and Desaulniers (2005) proposed the first algorithm. In this case, the set `F` must be known beforehand. This transformation is slightly fast, but generates bigger graphs `G*`. Each forbidden path can be of different size, but no sub-path (of at least three nodes long) can be part of another forbidden path.
1. Hsu et al. 'Backward Construction' (2009) solves the restriction of sub-paths in the forbidden paths, and generates smaller graphs `G*`, by adding less new nodes and arcs. However, this algorithm is slightly slower.
Both algorithms are analyzed using 27 graphs, randomly generated. The complete benchmark evaluation [can be found here](articles/benchmark.html).
## Installation
As from 2018-11-22 you can install **rsppfp** directly from CRAN, using:
```{r cran-install, eval = FALSE}
install.packages("rsppfp")
```
You can install the development version of rsppfp from GitHub with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("melvidoni/rsppfp")
```
## References
Available at [References](articles/references.html)