The TSP package (Hahsler and Hornik 2007) provides the basic infrastructure and some algorithms for the traveling salesman problems (symmetric, asymmetric and Euclidean TSPs). The package provides some fast implementations of simple algorithms including:
- Tour construction heuristics
- Insertion algorithms: nearest insertion, farthest insertion, cheapest insertion, arbitrary insertion (Rosenkrantz, Stearns, and Philip M. Lewis 1977)
- Nearest neighbor methods: Nearest neighbor and repetitive nearest neighbor (Rosenkrantz, Stearns, and Philip M. Lewis 1977)
- Tour improvement methods
- Two-opt heuristic (Croes 1958)
- Simulated annealing (Kirkpatrick, Gelatt, and Vecchi 1983)
- State-of-the-art solver interfaces
- Concorde TSP solver interface (Applegate et al. 2000, 2006)
- Concorde Chained-Lin-Kernighan heuristic interface (Applegate, Cook, and Rohe 2003)
The package can read and write the TSPLIB format (Reinelt 1991) and it can solve many of the problems in the TSPLIB95 problem library (local copy of the archive).
The following R packages use TSP:
archetypes,
cholera,
condvis,
CRTspat,
ForagingOrg,
ggEDA,
isocir,
jocre,
MLCOPULA,
nilde,
nlnet,
PairViz,
pencopulaCond,
SCORPIUS,
sensitivity,
seriation,
sfnetworks,
tspmeta,
VineCopula
To cite package ‘TSP’ in publications use:
Hahsler M, Hornik K (2007). “TSP - Infrastructure for the traveling salesperson problem.” Journal of Statistical Software, 23(2), 1-21. ISSN 1548-7660, doi:10.18637/jss.v023.i02 https://doi.org/10.18637/jss.v023.i02.
@Article{,
title = {TSP -- {I}nfrastructure for the traveling salesperson problem},
author = {Michael Hahsler and Kurt Hornik},
year = {2007},
journal = {Journal of Statistical Software},
volume = {23},
number = {2},
pages = {1--21},
doi = {10.18637/jss.v023.i02},
month = {December},
issn = {1548-7660},
}
Stable CRAN version: Install from within R with
install.packages("TSP")Current development version: Install from r-universe.
install.packages("TSP",
repos = c("https://mhahsler.r-universe.dev",
"https://cloud.r-project.org/"))Load a data set with 312 cities (USA and Canada) and create a TSP object.
library("TSP")
data("USCA312")
tsp <- TSP(USCA312)
tsp## object of class 'TSP'
## 312 cities (distance 'euclidean')
Find a tour using the default heuristic.
tour <- solve_TSP(tsp)
tour## object of class 'TOUR'
## result of method 'arbitrary_insertion+two_opt' for 312 cities
## tour length: 40653
Show the first few cities in the tour.
head(tour, n = 10)## Daytona Beach, FL Jacksonville, FL Columbus, GA Atlanta, GA
## 72 127 65 12
## Macon, GA Augusta, GA Savannah, GA Charleston, SC
## 157 14 250 52
## Columbia, SC Wilmington, NC
## 64 303
Visualize the complete tour.
library(maps)
data("USCA312_GPS")
plot((USCA312_GPS[, c("long", "lat")]), cex = 0.3)
map("world", col = "gray", add = TRUE)
polygon(USCA312_GPS[, c("long", "lat")][tour, ], border = "red")
An online example application of TSP can be found on shinyapps.
You can find Q&A’s and ask your own questions at https://stackoverflow.com/search?q=TSP+R
Please submit bug reports to https://github.com/mhahsler/TSP/issues
Applegate, David, Robert E. Bixby, Vasek Chvátal, and William Cook. 2000. “TSP Cuts Which Do Not Conform to the Template Paradigm.” In Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions, edited by M. Junger and D. Naddef, 2241:261–304. Lecture Notes in Computer Science. London, UK: Springer-Verlag. https://doi.org/10.1007/3-540-45586-8_7.
Applegate, David, Robert Bixby, Vasek Chvátal, and William Cook. 2006. Concorde TSP Solver. https://www.math.uwaterloo.ca/tsp/concorde.html.
Applegate, David, William Cook, and Andre Rohe. 2003. “Chained Lin-Kernighan for Large Traveling Salesman Problems.” INFORMS Journal on Computing 15 (1): 82–92. https://doi.org/10.1287/ijoc.15.1.82.15157.
Croes, G. A. 1958. “A Method for Solving Traveling-Salesman Problems.” Operations Research 6 (6): 791–812. https://doi.org/10.1287/opre.6.6.791.
Hahsler, Michael, and Kurt Hornik. 2007. “TSP – Infrastructure for the Traveling Salesperson Problem.” Journal of Statistical Software 23 (2): 1–21. https://doi.org/10.18637/jss.v023.i02.
Kirkpatrick, S., C. D. Gelatt, and M. P. Vecchi. 1983. “Optimization by Simulated Annealing.” Science 220 (4598): 671–80. https://doi.org/10.1126/science.220.4598.671.
Reinelt, Gerhard. 1991. “TSPLIB—a Traveling Salesman Problem Library.” ORSA Journal on Computing 3 (4): 376–84. https://doi.org/10.1287/ijoc.3.4.376.
Rosenkrantz, Daniel J., Richard E. Stearns, and II Philip M. Lewis. 1977. “An Analysis of Several Heuristics for the Traveling Salesman Problem.” SIAM Journal on Computing 6 (3): 563–81. https://doi.org/10.1007/978-1-4020-9688-4_3.