Package 'TSP' reference manual

Title:	Infrastructure for the Traveling Salesperson Problem
Description:	Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. The code for Concorde itself is not included in the package and has to be obtained separately. Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
Authors:	Michael Hahsler [aut, cre, cph] , Kurt Hornik [aut, cph]
Maintainer:	Michael Hahsler <[email protected]>
License:	GPL-3
Version:	1.2-4.1
Built:	2024-12-26 05:28:21 UTC
Source:	https://github.com/mhahsler/TSP

Class ATSP – Asymmetric traveling salesperson problem

Description

Constructor to create an instance of the asymmetric traveling salesperson problem (ATSP) and some auxiliary methods.

Usage

ATSP(x, labels = NULL, method = NULL)

as.ATSP(x)

## S3 method for class 'matrix'
as.ATSP(x)

## S3 method for class 'dist'
as.ATSP(x)

## S3 method for class 'ATSP'
print(x, ...)

## S3 method for class 'ATSP'
n_of_cities(x)

## S3 method for class 'ATSP'
labels(object, ...)

## S3 method for class 'ATSP'
image(x, order, col = gray.colors(64), ...)

## S3 method for class 'ATSP'
as.matrix(x, ...)
ATSP(x, labels = NULL, method = NULL)

as.ATSP(x)

## S3 method for class 'matrix'
as.ATSP(x)

## S3 method for class 'dist'
as.ATSP(x)

## S3 method for class 'ATSP'
print(x, ...)

## S3 method for class 'ATSP'
n_of_cities(x)

## S3 method for class 'ATSP'
labels(object, ...)

## S3 method for class 'ATSP'
image(x, order, col = gray.colors(64), ...)

## S3 method for class 'ATSP'
as.matrix(x, ...)

Arguments

`x`, `object`	an object (a square matrix) to be converted into an `ATSP` or, for the methods, an object of class `ATSP`.
`labels`	optional city labels. If not given, labels are taken from `x`.
`method`	optional name of the distance metric.
`...`	further arguments are passed on.
`order`	order of cities as an integer vector or an object of class `TOUR`.
`col`	color scheme for image.

Details

Objects of class ATSP are internally represented by a matrix (use as.matrix() to get just the matrix).

ATSPs can be transformed into (larger) symmetric TSPs using reformulate_ATSP_as_TSP().

Value

ATSP() returns x as an object of class ATSP.
n_of_cities() returns the number of cities in x.
labels() returns a vector with the names of the cities in x.

Author(s)

Michael Hahsler

Examples

data <- matrix(runif(10^2), ncol = 10, dimnames = list(1:10, 1:10))

atsp <- ATSP(data)
atsp

## use some methods
n_of_cities(atsp)
labels(atsp)

## calculate a tour
tour <- solve_TSP(atsp, method = "nn")
tour

tour_length(tour)

image(atsp, tour)
data <- matrix(runif(10^2), ncol = 10, dimnames = list(1:10, 1:10))

atsp <- ATSP(data)
atsp

## use some methods
n_of_cities(atsp)
labels(atsp)

## calculate a tour
tour <- solve_TSP(atsp, method = "nn")
tour

tour_length(tour)

image(atsp, tour)

Using the Concorde TSP Solver

Description

The Concorde TSP Solver package contains several solvers. Currently, interfaces to the Concorde solver (Applegate et al. 2001), one of the most advanced and fastest TSP solvers using branch-and-cut, and the Chained Lin-Kernighan (Applegate et al. 2003) implementation are provided in TSP. Concorde can solve TSPs and ETSPs directly. ATSPs are reformulated as larger TSP's and then solved.

Usage

concorde_path(path)

concorde_help()

linkern_help()
concorde_path(path)

concorde_help()

linkern_help()

Arguments

path

a character string with the path to the directory where the executables are installed.

Details

Installation of Concorde

The Concorde TSP Solver is freely available for academic research. It is not included in the TSP R package and has to be obtained separately from the Concorde download page. Either download the precompiled executables and place them in a suitable directory (make sure they are executable), or you can get the source code and compile the program on your own. TSP needs to know where the executables are. There are two options:

use concorde_path() to set the path to the directory containing the executables for concorde and linkern, or
make sure that the executables are in the search path stored in the PATH environment variable (see Sys.setenv()).

Using Concorde for solve_TSP()

solve_TSP() uses write_TSPLIB() to write the TSP for Concorde and tries to find the appropriate precision value (digits after the decimal point) to convert the provided distances into the needed integer value range. The precision value can also be specified in control in solve_TSP() with method Concorde. Warning messages will alert the user if the conversion to integer values results into rounding errors that are worse then what is specified in the precision control parameter.

To get a list of all available command line options which can be used via the clo option for solve_TSP use concorde_help() and linkern_help(). Several options (-x, -o, -N, -Q) are not available via solve_TSP() since they are used by the interface.

If Concorde takes too long, then you can kill the 'concorde' process via your operating system and you can continue with R.

Value

Nothing.

Author(s)

Michael Hahsler

References

Concorde home page, http://www.math.uwaterloo.ca/tsp/concorde/

David Applegate, Robert Bixby, Vasek Chvatal, William Cook (2001): TSP cuts which do not conform to the template paradigm, Computational Combinatorial Optimization, M. Junger and D. Naddef (editors), Springer-Verlag.

David Applegate and William Cook and Andre Rohe (2003): Chained Lin-Kernighan for Large Traveling Salesman Problems, INFORMS Journal on Computing, 15, 82–92.

Examples


## Not run: 
## see if Concorde is correctly installed
concorde_path()


## set path to the Concorde executible if it is not in the search PATH
## Example:
## concorde_path("~/concorde/")

concorde_help()

data("USCA312")

## run concorde in verbose mode (-v) with fast cuts only (-V)
solve_TSP(USCA312, method = "concorde", control = list(clo = "-v -V"))

## End(Not run)

## Not run: 
## see if Concorde is correctly installed
concorde_path()


## set path to the Concorde executible if it is not in the search PATH
## Example:
## concorde_path("~/concorde/")

concorde_help()

data("USCA312")

## run concorde in verbose mode (-v) with fast cuts only (-V)
solve_TSP(USCA312, method = "concorde", control = list(clo = "-v -V"))

## End(Not run)

Cut a tour to form a path

Description

Cuts a tour at a specified city to form a path.

Usage

cut_tour(x, cut, exclude_cut = TRUE)

## S3 method for class 'TOUR'
cut_tour(x, cut, exclude_cut = TRUE)
cut_tour(x, cut, exclude_cut = TRUE)

## S3 method for class 'TOUR'
cut_tour(x, cut, exclude_cut = TRUE)

Arguments

`x`	an object of class TOUR.
`cut`	the index or label of the city/cities to cut the tour.
`exclude_cut`	exclude the city where we cut? If `FALSE`, the city at the cut is included in the path as the first city.

Value

Returns a named vector with city ids forming the path. If multiple cuts are used then a list with paths is returned.

Author(s)

Michael Hahsler

Examples

data("USCA50")

## find a path starting at Austin, TX
tour <- solve_TSP(USCA50)
path <- cut_tour(tour, cut = "Austin, TX", exclude_cut = FALSE)
path

## cut the tours at two cities
tour <- solve_TSP(USCA50)
path <- cut_tour(tour, cut = c("Austin, TX", "Cambridge, MA"), exclude_cut = FALSE)
path

## cut a tour at the largest gap using a dummy city
tsp <- insert_dummy(USCA50, label = "cut")
tour <- solve_TSP(tsp)

## cut tour into path at the dummy city
path <- cut_tour(tour, "cut")
path
data("USCA50")

## find a path starting at Austin, TX
tour <- solve_TSP(USCA50)
path <- cut_tour(tour, cut = "Austin, TX", exclude_cut = FALSE)
path

## cut the tours at two cities
tour <- solve_TSP(USCA50)
path <- cut_tour(tour, cut = c("Austin, TX", "Cambridge, MA"), exclude_cut = FALSE)
path

## cut a tour at the largest gap using a dummy city
tsp <- insert_dummy(USCA50, label = "cut")
tour <- solve_TSP(tsp)

## cut tour into path at the dummy city
path <- cut_tour(tour, "cut")
path

Class ETSP – Euclidean traveling salesperson problem

Description

Constructor to create an instance of a Euclidean traveling salesperson problem (TSP) represented by city coordinates and some auxiliary methods.

Usage

ETSP(x, labels = NULL)

as.ETSP(x)

## S3 method for class 'matrix'
as.ETSP(x)

## S3 method for class 'data.frame'
as.ETSP(x)

## S3 method for class 'ETSP'
as.TSP(x)

## S3 method for class 'ETSP'
as.matrix(x, ...)

## S3 method for class 'ETSP'
print(x, ...)

## S3 method for class 'ETSP'
n_of_cities(x)

## S3 method for class 'ETSP'
labels(object, ...)

## S3 method for class 'ETSP'
image(x, order, col = gray.colors(64), ...)

## S3 method for class 'ETSP'
plot(x, y = NULL, tour = NULL, tour_lty = 2, tour_col = 2, labels = TRUE, ...)
ETSP(x, labels = NULL)

as.ETSP(x)

## S3 method for class 'matrix'
as.ETSP(x)

## S3 method for class 'data.frame'
as.ETSP(x)

## S3 method for class 'ETSP'
as.TSP(x)

## S3 method for class 'ETSP'
as.matrix(x, ...)

## S3 method for class 'ETSP'
print(x, ...)

## S3 method for class 'ETSP'
n_of_cities(x)

## S3 method for class 'ETSP'
labels(object, ...)

## S3 method for class 'ETSP'
image(x, order, col = gray.colors(64), ...)

## S3 method for class 'ETSP'
plot(x, y = NULL, tour = NULL, tour_lty = 2, tour_col = 2, labels = TRUE, ...)

Arguments

`x`, `object`	an object (data.frame or matrix) to be converted into a `ETSP` or, for the methods, an object of class `ETSP`.
`labels`	logical; plot city labels.
`...`	further arguments are passed on.
`order`	order of cities for the image as an integer vector or an object of class TOUR.
`col`	color scheme for image.
`tour`, `y`	a tour to be visualized.
`tour_lty`, `tour_col`	line type and color for tour.

Details

Objects of class ETSP are internally represented as a matrix objects (use as.matrix() to get the matrix object).

Value

ETSP() returns x as an object of class ETSP.
n_of_cities() returns the number of cities in x.
labels() returns a vector with the names of the cities in x.

Author(s)

Michael Hahsler

Examples

## create a random ETSP
n <- 20
x <- data.frame(x = runif(n), y = runif(n), row.names = LETTERS[1:n])
etsp <- ETSP(x)
etsp

## use some methods
n_of_cities(etsp)
labels(etsp)

## plot ETSP and solution
tour <- solve_TSP(etsp)
tour

plot(etsp, tour, tour_col = "red")

# plot with custom labels
plot(etsp, tour, tour_col = "red", labels = FALSE)
text(etsp, paste("City", rownames(etsp)), pos = 1)
## create a random ETSP
n <- 20
x <- data.frame(x = runif(n), y = runif(n), row.names = LETTERS[1:n])
etsp <- ETSP(x)
etsp

## use some methods
n_of_cities(etsp)
labels(etsp)

## plot ETSP and solution
tour <- solve_TSP(etsp)
tour

plot(etsp, tour, tour_col = "red")

# plot with custom labels
plot(etsp, tour, tour_col = "red", labels = FALSE)
text(etsp, paste("City", rownames(etsp)), pos = 1)

Insert dummy cities into a distance matrix

Description

Inserts dummy cities into a TSP problem. A dummy city has the same, constant distance (0) to all other cities and is infinitely far from other dummy cities. A dummy city can be used to transform a shortest Hamiltonian path problem (i.e., finding an optimal linear order) into a shortest Hamiltonian cycle problem which can be solved by a TSP solvers (Garfinkel 1985).

Usage

insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'TSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'ATSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'ETSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'TSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'ATSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

## S3 method for class 'ETSP'
insert_dummy(x, n = 1, const = 0, inf = Inf, label = "dummy")

Arguments

`x`	an object with a TSP problem.
`n`	number of dummy cities.
`const`	distance of the dummy cities to all other cities.
`inf`	distance between dummy cities.
`label`	labels for the dummy cities. If only one label is given, it is reused for all dummy cities.

Details

Several dummy cities can be used together with a TSP solvers to perform rearrangement clustering (Climer and Zhang 2006).

The dummy cities are inserted after the other cities in x.

A const of 0 is guaranteed to work if the TSP finds the optimal solution. For heuristics returning suboptimal solutions, a higher const (e.g., 2 * max(x)) might provide better results.

Value

returns an object of the same class as x.

Author(s)

Michael Hahsler

References

Sharlee Climer, Weixiong Zhang (2006): Rearrangement Clustering: Pitfalls, Remedies, and Applications, Journal of Machine Learning Research 7(Jun), pp. 919–943.

R.S. Garfinkel (1985): Motivation and modelling (chapter 2). In: E. L. Lawler, J. K. Lenstra, A.H.G. Rinnooy Kan, D. B. Shmoys (eds.) The traveling salesman problem - A guided tour of combinatorial optimization, Wiley & Sons.

Examples

## Example 1: Find a short Hamiltonian path
set.seed(1000)
x <- data.frame(x = runif(20), y = runif(20), row.names = LETTERS[1:20])

tsp <- TSP(dist(x))

## add a dummy city to cut the tour into a path
tsp <- insert_dummy(tsp, label = "cut")
tour <- solve_TSP(tsp)
tour

plot(x)
lines(x[cut_tour(tour, cut = "cut"),])


## Example 2: Rearrangement clustering of the iris dataset
set.seed(1000)
data("iris")
tsp <- TSP(dist(iris[-5]))

## insert 2 dummy cities to creates 2 clusters
tsp_dummy <- insert_dummy(tsp, n = 3, label = "boundary")

## get a solution for the TSP
tour <- solve_TSP(tsp_dummy)

## plot the reordered distance matrix with the dummy cities as lines separating
## the clusters
image(tsp_dummy, tour)
abline(h = which(labels(tour)=="boundary"), col = "red")
abline(v = which(labels(tour)=="boundary"), col = "red")

## plot the original data with paths connecting the points in each cluster
plot(iris[,c(2,3)], col = iris[,5])
paths <- cut_tour(tour, cut = "boundary")
for(p in paths) lines(iris[p, c(2,3)])

## Note: The clustering is not perfect!
## Example 1: Find a short Hamiltonian path
set.seed(1000)
x <- data.frame(x = runif(20), y = runif(20), row.names = LETTERS[1:20])

tsp <- TSP(dist(x))

## add a dummy city to cut the tour into a path
tsp <- insert_dummy(tsp, label = "cut")
tour <- solve_TSP(tsp)
tour

plot(x)
lines(x[cut_tour(tour, cut = "cut"),])


## Example 2: Rearrangement clustering of the iris dataset
set.seed(1000)
data("iris")
tsp <- TSP(dist(iris[-5]))

## insert 2 dummy cities to creates 2 clusters
tsp_dummy <- insert_dummy(tsp, n = 3, label = "boundary")

## get a solution for the TSP
tour <- solve_TSP(tsp_dummy)

## plot the reordered distance matrix with the dummy cities as lines separating
## the clusters
image(tsp_dummy, tour)
abline(h = which(labels(tour)=="boundary"), col = "red")
abline(v = which(labels(tour)=="boundary"), col = "red")

## plot the original data with paths connecting the points in each cluster
plot(iris[,c(2,3)], col = iris[,5])
paths <- cut_tour(tour, cut = "boundary")
for(p in paths) lines(iris[p, c(2,3)])

## Note: The clustering is not perfect!

Reformulate a ATSP as a symmetric TSP

Description

A ATSP can be formulated as a symmetric TSP by doubling the number of cities (Jonker and Volgenant 1983). The solution of the TSP also represents the solution of the original ATSP.

Usage

reformulate_ATSP_as_TSP(x, infeasible = Inf, cheap = -Inf)

filter_ATSP_as_TSP_dummies(tour, atsp)
reformulate_ATSP_as_TSP(x, infeasible = Inf, cheap = -Inf)

filter_ATSP_as_TSP_dummies(tour, atsp)

Arguments

`x`	an ATSP.
`infeasible`	value for infeasible connections.
`cheap`	value for distance between a city and its corresponding dummy city.
`tour`	a TOUR created for a ATSP reformulated as a TSP.
`atsp`	the original ATSP.

Details

To reformulate a ATSP as a TSP, for each city a dummy city (e.g, for 'New York' a dummy city 'New York*') is added. Between each city and its corresponding dummy city a very small (or negative) distance with value cheap is used. To ensure that the solver places each cities always occurs in the solution together with its dummy city, this cost has to be much smaller than the distances in the TSP. The original distances are used between the cities and the dummy cities, where each city is responsible for the distance going to the city and the dummy city is responsible for the distance coming from the city. The distances between all cities and the distances between all dummy cities are set to infeasible, a very large value which prevents the solver from using these links. We use infinite values here and solve_TSP() treats them appropriately.

filter_ATSP_as_TSP_dummies() can be used to extract the solution for the original ATSP from the tour found for an ATSP reformulated as a TSP. Note that the symmetric TSP tour does not reveal the direction for the ATSP. The filter function computed the tour length for both directions and returns the shorter tour.

solve_TSP() has a parameter as_TSP which preforms the reformulation and filtering the dummy cities automatically.

Note on performance: Doubling the problem size is a performance issue especially has a negative impact on solution quality for heuristics. It should only be used together with Concorde when the optimal solution is required. Most heuristics can solve ATSPs directly with good solution quality.

Value

reformulate_ATSP_as_TSP() returns a TSP object. filter_ATSP_as_TSP_dummies() returns a TOUR object.

Author(s)

Michael Hahsler

References

Jonker, R. and Volgenant, T. (1983): Transforming asymmetric into symmetric traveling salesman problems, Operations Research Letters, 2, 161–163.

Examples

data("USCA50")

## set the distances from anywhere to Austin to zero which makes it an ATSP
austin <- which(labels(USCA50) == "Austin, TX")
atsp <- as.ATSP(USCA50)
atsp[, austin] <- 0
atsp

## reformulate as a TSP (by doubling the number of cities with dummy cities marked with *)
tsp <- reformulate_ATSP_as_TSP(atsp)
tsp

## create tour for the TSP. You should use Concorde to find the optimal solution.
# tour_tsp <- solve_TSP(tsp, method = "concorde")
# The standard heuristic is bad for this problem. We use it here because
#   Concord may not be installed.
tour_tsp <- solve_TSP(tsp)
head(labels(tour_tsp), n = 10)
tour_tsp
# The tour length is -Inf since it includes cheap links
#  from a city to its dummy city.

## get the solution for the original ATSP by filtering out the dummy cities.
tour_atsp <- filter_ATSP_as_TSP_dummies(tour_tsp, atsp = atsp)
tour_atsp
head(labels(tour_atsp), n = 10)

## This process can also be done automatically by using as_TSP = TRUE:
# solve_TSP(atsp, method = "concorde", as_TSP = TRUE)

## The default heuristic can directly solve ATSPs with results close to the
#  optimal solution of 12715.
solve_TSP(atsp, control = list(rep = 10))
data("USCA50")

## set the distances from anywhere to Austin to zero which makes it an ATSP
austin <- which(labels(USCA50) == "Austin, TX")
atsp <- as.ATSP(USCA50)
atsp[, austin] <- 0
atsp

## reformulate as a TSP (by doubling the number of cities with dummy cities marked with *)
tsp <- reformulate_ATSP_as_TSP(atsp)
tsp

## create tour for the TSP. You should use Concorde to find the optimal solution.
# tour_tsp <- solve_TSP(tsp, method = "concorde")
# The standard heuristic is bad for this problem. We use it here because
#   Concord may not be installed.
tour_tsp <- solve_TSP(tsp)
head(labels(tour_tsp), n = 10)
tour_tsp
# The tour length is -Inf since it includes cheap links
#  from a city to its dummy city.

## get the solution for the original ATSP by filtering out the dummy cities.
tour_atsp <- filter_ATSP_as_TSP_dummies(tour_tsp, atsp = atsp)
tour_atsp
head(labels(tour_atsp), n = 10)

## This process can also be done automatically by using as_TSP = TRUE:
# solve_TSP(atsp, method = "concorde", as_TSP = TRUE)

## The default heuristic can directly solve ATSPs with results close to the
#  optimal solution of 12715.
solve_TSP(atsp, control = list(rep = 10))

TSP solver interface

Description

Common interface to all TSP solvers in this package.

Usage

solve_TSP(x, method = NULL, control = NULL, ...)

## S3 method for class 'TSP'
solve_TSP(x, method = NULL, control = NULL, ...)

## S3 method for class 'ATSP'
solve_TSP(x, method = NULL, control = NULL, as_TSP = FALSE, ...)

## S3 method for class 'ETSP'
solve_TSP(x, method = NULL, control = NULL, ...)
solve_TSP(x, method = NULL, control = NULL, ...)

## S3 method for class 'TSP'
solve_TSP(x, method = NULL, control = NULL, ...)

## S3 method for class 'ATSP'
solve_TSP(x, method = NULL, control = NULL, as_TSP = FALSE, ...)

## S3 method for class 'ETSP'
solve_TSP(x, method = NULL, control = NULL, ...)

Arguments

`x`	a TSP problem.
`method`	method to solve the TSP (default: "arbitrary insertion" algorithm with two_opt refinement.
`control`	a list of arguments passed on to the TSP solver selected by `method`.
`...`	additional arguments are added to `control`.
`as_TSP`	should the ATSP reformulated as a TSP for the solver?

Details

TSP Methods

Currently the following methods are available:

"identity", "random" return a tour representing the order in the data (identity order) or a random order. [TSP, ATSP]
"nearest_insertion", "farthest_insertion", "cheapest_insertion", "arbitrary_insertion" Nearest, farthest, cheapest and arbitrary insertion algorithms for a symmetric and asymmetric TSP (Rosenkrantz et al. 1977). [TSP, ATSP]

The distances between cities are stored in a distance matrix $D$ with elements $d(i,j)$ . All insertion algorithms start with a tour consisting of an arbitrary city and choose in each step a city $k$ not yet on the tour. This city is inserted into the existing tour between two consecutive cities $i$ and $j$ , such that

$d(i,k) + d(k,j) - d(i,j)$

is minimized. The algorithms stops when all cities are on the tour.

The nearest insertion algorithm chooses city $k$ in each step as the city which is nearest to a city on the tour.

For farthest insertion, the city $k$ is chosen in each step as the city which is farthest to any city on the tour.

Cheapest insertion chooses the city $k$ such that the cost of inserting the new city (i.e., the increase in the tour's length) is minimal.

Arbitrary insertion chooses the city $k$ randomly from all cities not yet on the tour.

Nearest and cheapest insertion tries to build the tour using cities which fit well into the partial tour constructed so far. The idea behind behind farthest insertion is to link cities far away into the tour fist to establish an outline of the whole tour early.

Additional control options:
- "start" index of the first city (default: a random city).
"nn", "repetitive_nn" Nearest neighbor and repetitive nearest neighbor algorithms for symmetric and asymmetric TSPs (Rosenkrantz et al. 1977). [TSP, ATSP]

The algorithm starts with a tour containing a random city. Then the algorithm always adds to the last city on the tour the nearest not yet visited city. The algorithm stops when all cities are on the tour.

Repetitive nearest neighbor constructs a nearest neighbor tour for each city as the starting point and returns the shortest tour found.

Additional control options:
- "start" index of the first city (default: a random city).
"two_opt" Two edge exchange improvement procedure (Croes 1958). [TSP, ATSP]

This is a tour refinement procedure which systematically exchanges two edges in the graph represented by the distance matrix till no improvements are possible. Exchanging two edges is equal to reversing part of the tour. The resulting tour is called 2-optimal.

This method can be applied to tours created by other methods or used as its own method. In this case improvement starts with a random tour.

Additional control options:
- "tour" an existing tour which should be improved. If no tour is given, a random tour is used.
- "two_opt_repetitions" number of times to try two_opt with a different initial random tour (default: 1).
"concorde" Concorde algorithm (Applegate et al. 2001). [TSP, ETSP]

Concorde is an advanced exact TSP solver for symmetric TSPs based on branch-and-cut. ATSPs can be solved using reformulate_ATSP_as_TSP() done automatically with as_TSP = TRUE. The program is not included in this package and has to be obtained and installed separately.

Additional control options:
- "exe" a character string containing the path to the executable (see Concorde).
- "clo" a character string containing command line options for Concorde, e.g., control = list(clo = "-B -v"). See concorde_help() on how to obtain a complete list of available command line options.
- "precision" an integer which controls the number of decimal places used for the internal representation of distances in Concorde. The values given in x are multiplied by $10^{precision}$ before being passed on to Concorde. Note that therefore the results produced by Concorde (especially lower and upper bounds) need to be divided by $10^{precision}$ (i.e., the decimal point has to be shifted precision placed to the left). The interface to Concorde uses write_TSPLIB().
"linkern" Concorde's Chained Lin-Kernighan heuristic (Applegate et al. 2003). [TSP, ETSP]

The Lin-Kernighan (Lin and Kernighan 1973) heuristic uses variable $k$ edge exchanges to improve an initial tour. The program is not included in this package and has to be obtained and installed separately (see Concorde).

Additional control options: see Concorde above.

Treatment of NAs and infinite values in x

TSP and ATSP need to contain valid distances. NAs are not allowed. Inf is allowed and can be used to model the missing edges in incomplete graphs (i.e., the distance between the two objects is infinite) or infeasible connections. Internally, Inf is replaced by a large value given by $max(x) + 2 range(x)$ . Note that the solution might still place the two objects next to each other (e.g., if x contains several unconnected subgraphs) which results in a path length of Inf. -Inf is replaced by $min(x) - 2 range(x)$ and can be used to encourage the solver to place two objects next to each other.

Parallel execution support

All heuristics can be used with the control arguments repetitions (uses the best from that many repetitions with random starts) and two_opt (a logical indicating if two_opt refinement should be performed). If several repetitions are done (this includes method "repetitive_nn") then foreach is used so they can be performed in parallel on multiple cores/machines. To enable parallel execution an appropriate parallel backend needs to be registered (e.g., load doParallel and register it with doParallel::registerDoParallel()).

Solving ATSP and ETSP

Some solvers (including Concorde) cannot directly solve ATSP directly. ATSP can be reformulated as larger TSP and solved this way. For convenience, solve_TSP() has an extra argument as_TSP which can be set to TRUE to automatically solve the ATSP reformulated as a TSP (see reformulate_ATSP_as_TSP()).

Only methods "concorde" and "linkern" can solve ETSPs directly. For all other methods, ETSPs are currently converted into TSPs by creating a distance matrix and then solved.

Value

An object of class TOUR.

Author(s)

Michael Hahsler

References

D. Applegate, W. Cook and A. Rohe (2003): Chained Lin-Kernighan for Large Traveling Salesman Problems. INFORMS Journal on Computing, 15(1):82–92.

G.A. Croes (1958): A method for solving traveling-salesman problems. Operations Research, 6(6):791–812.

S. Lin and B. Kernighan (1973): An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21(2): 498–516.

D.J. Rosenkrantz, R. E. Stearns, and Philip M. Lewis II (1977): An analysis of several heuristics for the traveling salesman problem. SIAM Journal on Computing, 6(3):563–581.

Examples


## solve a simple Euclidean TSP (using the default method)
etsp <- ETSP(data.frame(x = runif(20), y = runif(20)))
tour <- solve_TSP(etsp)
tour
tour_length(tour)
plot(etsp, tour)


## compare methods
data("USCA50")
USCA50
methods <- c("identity", "random", "nearest_insertion",
  "cheapest_insertion", "farthest_insertion", "arbitrary_insertion",
  "nn", "repetitive_nn", "two_opt")

## calculate tours
tours <- lapply(methods, FUN = function(m) solve_TSP(USCA50, method = m))
names(tours) <- methods

## use the external solver which has to be installed separately
## Not run: 
tours$concorde  <- solve_TSP(USCA50, method = "concorde")
tours$linkern  <- solve_TSP(USCA50, method = "linkern")

## End(Not run)

## register a parallel backend to perform repetitions in parallel
## Not run: 
library(doParallel)
registerDoParallel()

## End(Not run)

## add some tours using repetition and two_opt refinements
tours$'nn+two_opt' <- solve_TSP(USCA50, method = "nn", two_opt = TRUE)
tours$'nn+rep_10' <- solve_TSP(USCA50, method = "nn", rep = 10)
tours$'nn+two_opt+rep_10' <- solve_TSP(USCA50, method = "nn", two_opt = TRUE, rep = 10)
tours$'arbitrary_insertion+two_opt' <- solve_TSP(USCA50)

## show first tour
tours[[1]]

## compare tour lengths
opt <- 14497 # obtained by Concorde
tour_lengths <- c(sort(sapply(tours, tour_length), decreasing = TRUE),
  optimal = opt)
dotchart(tour_lengths / opt * 100 - 100, xlab = "percent excess over optimum")
## solve a simple Euclidean TSP (using the default method)
etsp <- ETSP(data.frame(x = runif(20), y = runif(20)))
tour <- solve_TSP(etsp)
tour
tour_length(tour)
plot(etsp, tour)


## compare methods
data("USCA50")
USCA50
methods <- c("identity", "random", "nearest_insertion",
  "cheapest_insertion", "farthest_insertion", "arbitrary_insertion",
  "nn", "repetitive_nn", "two_opt")

## calculate tours
tours <- lapply(methods, FUN = function(m) solve_TSP(USCA50, method = m))
names(tours) <- methods

## use the external solver which has to be installed separately
## Not run: 
tours$concorde  <- solve_TSP(USCA50, method = "concorde")
tours$linkern  <- solve_TSP(USCA50, method = "linkern")

## End(Not run)

## register a parallel backend to perform repetitions in parallel
## Not run: 
library(doParallel)
registerDoParallel()

## End(Not run)

## add some tours using repetition and two_opt refinements
tours$'nn+two_opt' <- solve_TSP(USCA50, method = "nn", two_opt = TRUE)
tours$'nn+rep_10' <- solve_TSP(USCA50, method = "nn", rep = 10)
tours$'nn+two_opt+rep_10' <- solve_TSP(USCA50, method = "nn", two_opt = TRUE, rep = 10)
tours$'arbitrary_insertion+two_opt' <- solve_TSP(USCA50)

## show first tour
tours[[1]]

## compare tour lengths
opt <- 14497 # obtained by Concorde
tour_lengths <- c(sort(sapply(tours, tour_length), decreasing = TRUE),
  optimal = opt)
dotchart(tour_lengths / opt * 100 - 100, xlab = "percent excess over optimum")

Class TOUR – Solution to a traveling salesperson problem

Description

Class to store the solution of a TSP. Objects of this class are returned by TSP solvers in this package. Essentially, an object of class TOUR is a permutation vector containing the order of cities to visit.

Usage

TOUR(x, method = NA, tsp = NULL)

as.TOUR(object)

## S3 method for class 'numeric'
as.TOUR(object)

## S3 method for class 'integer'
as.TOUR(object)

## S3 method for class 'TOUR'
print(x, ...)
TOUR(x, method = NA, tsp = NULL)

as.TOUR(object)

## S3 method for class 'numeric'
as.TOUR(object)

## S3 method for class 'integer'
as.TOUR(object)

## S3 method for class 'TOUR'
print(x, ...)

Arguments

`x`	an integer permutation vector or, for the methods an object of class TOUR.
`method`	character string; method used to create the tour.
`tsp`	`TSP` object the tour applies to. If available then the tour will include the tour length. Also the labels of the cities will be available in the tour (otherwise the labels of `x` are used).
`object`	data (an integer vector) which can be coerced to `TOUR`.
`...`	further arguments are passed on.

Details

Since an object of class TOUR is an integer vector, it can be subsetted as an ordinary vector or coerced to an integer vector using as.integer(). It also contains the names of the objects as labels. Additionally, TOUR has the following attributes: "method", "tour_length".

For most functions, e.g., tour_length() or image.TSP(), the TSP/ATSP object used to find the tour is still needed, since the tour does not contain the distance information.

Author(s)

Michael Hahsler

Examples

TOUR(1:10)

## calculate a tour
data("USCA50")
tour <- solve_TSP(USCA50)
tour

## get tour length directly from tour
tour_length(tour)

## get permutation vector
as.integer(tour)

## show labels
labels(tour)
TOUR(1:10)

## calculate a tour
data("USCA50")
tour <- solve_TSP(USCA50)
tour

## get tour length directly from tour
tour_length(tour)

## get permutation vector
as.integer(tour)

## show labels
labels(tour)

Calculate the length of a tour

Description

Calculate the length of a TOUR for a TSP.

Usage

tour_length(x, ...)

## S3 method for class 'TSP'
tour_length(x, order, ...)

## S3 method for class 'ATSP'
tour_length(x, order, ...)

## S3 method for class 'ETSP'
tour_length(x, order, ...)

## S3 method for class 'TOUR'
tour_length(x, tsp = NULL, ...)

## S3 method for class 'integer'
tour_length(x, tsp = NULL, ...)
tour_length(x, ...)

## S3 method for class 'TSP'
tour_length(x, order, ...)

## S3 method for class 'ATSP'
tour_length(x, order, ...)

## S3 method for class 'ETSP'
tour_length(x, order, ...)

## S3 method for class 'TOUR'
tour_length(x, tsp = NULL, ...)

## S3 method for class 'integer'
tour_length(x, tsp = NULL, ...)

Arguments

`x`	a TSP problem or a TOUR.
`...`	further arguments are currently unused.
`order`	an object of class `TOUR`
`tsp`	as TSP object.

Details

If no tsp is specified, then the tour length stored in x as attribute "tour_length" is returned. If tsp is given then the tour length is recalculated using the specified TSP problem.

If a distance in the tour is infinite, the result is also infinite. If the tour contains positive and negative infinite distances then the method returns NA.

Author(s)

Michael Hahsler

Examples


data("USCA50")

## original order
tour_length(solve_TSP(USCA50, method="identity"))

## length of a manually created (random) tour
tour <- TOUR(sample(seq(n_of_cities(USCA50))))
tour
tour_length(tour)
tour_length(tour, USCA50)
data("USCA50")

## original order
tour_length(solve_TSP(USCA50, method="identity"))

## length of a manually created (random) tour
tour <- TOUR(sample(seq(n_of_cities(USCA50))))
tour
tour_length(tour)
tour_length(tour, USCA50)

Class TSP – Symmetric traveling salesperson problem

Description

Constructor to create an instance of a symmetric traveling salesperson problem (TSP) and some auxiliary methods.

Usage

TSP(x, labels = NULL, method = NULL)

as.TSP(x)

## S3 method for class 'dist'
as.TSP(x)

## S3 method for class 'matrix'
as.TSP(x)

## S3 method for class 'TSP'
as.dist(m, ...)

## S3 method for class 'TSP'
print(x, ...)

n_of_cities(x)

## S3 method for class 'TSP'
n_of_cities(x)

## S3 method for class 'TSP'
labels(object, ...)

## S3 method for class 'TSP'
image(x, order, col = gray.colors(64), ...)
TSP(x, labels = NULL, method = NULL)

as.TSP(x)

## S3 method for class 'dist'
as.TSP(x)

## S3 method for class 'matrix'
as.TSP(x)

## S3 method for class 'TSP'
as.dist(m, ...)

## S3 method for class 'TSP'
print(x, ...)

n_of_cities(x)

## S3 method for class 'TSP'
n_of_cities(x)

## S3 method for class 'TSP'
labels(object, ...)

## S3 method for class 'TSP'
image(x, order, col = gray.colors(64), ...)

Arguments

`x`, `object`	an object (currently `dist` or a symmetric matrix) to be converted into a `TSP` or, for the methods, an object of class `TSP`.
`labels`	optional city labels. If not given, labels are taken from `x`.
`method`	optional name of the distance metric. If `x` is a `dist` object, then the method is taken from that object.
`m`	a TSP object to be converted to a dist object.
`...`	further arguments are passed on.
`order`	order of cities for the image as an integer vector or an object of class TOUR.
`col`	color scheme for image.

Details

Objects of class TSP are internally represented as dist objects (use as.dist() to get the dist object).

Not permissible paths can be set to a distance of +Inf. NAs are not allowed and -Inf will lead to the algorithm only being able to find an admissible tour, but not the best one.

Value

TSP() returns x as an object of class TSP.
n_of_cities() returns the number of cities in x.
labels() returns a vector with the names of the cities in x.

Author(s)

Michael Hahsler

Examples

data("iris")
d <- dist(iris[-5])

## create a TSP
tsp <- TSP(d)
tsp

## use some methods
n_of_cities(tsp)
labels(tsp)
image(tsp)
data("iris")
d <- dist(iris[-5])

## create a TSP
tsp <- TSP(d)
tsp

## use some methods
n_of_cities(tsp)
labels(tsp)
image(tsp)

Read and write TSPLIB files

Description

Reads and writes TSPLIB format files. TSPLIB files can be used by most TSP solvers. Sample instances for the TSP in TSPLIB format are available on the TSPLIB homepage (see references).

Usage

read_TSPLIB(file, precision = 0)

write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'TSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'ATSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'ETSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)
read_TSPLIB(file, precision = 0)

write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'TSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'ATSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

## S3 method for class 'ETSP'
write_TSPLIB(x, file, precision = 6, inf = NULL, neg_inf = NULL)

Arguments

`file`	file name or a connection.
`precision`	controls the number of decimal places used to represent distances (see details). If `x` already is `integer`, this argument is ignored and `x` is used as is.
`x`	an object with a TSP problem. `NA`s are not allowed.
`inf`	replacement value for `Inf` (TSPLIB format cannot handle `Inf`). If `inf` is `NULL`, a large value of $max(x) + 2 range(x)$ (ignoring infinite entries) is used.
`neg_inf`	replacement value for `-Inf`. If no value is specified, a small value of $min(x) - 2 range(x)$ (ignoring infinite entries) is used.

Details

In the TSPLIB format distances are represented by integer values. Therefore, if x contains double values (which is normal in R) the values given in x are multiplied by $10^{precision}$ before coercion to integer. Note that therefore all results produced by programs using the TSPLIB file as input need to be divided by $10^{precision}$ (i.e., the decimal point has to be shifted precision placed to the left).

Currently only the following EDGE_WEIGHT_TYPEs are implemented: EXPLICIT, EUC_2D and EUC_3D.

Value

returns an object of class TSP or ATSP.

Author(s)

Michael Hahsler

References

TSPLIB home page, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/

Examples


## Drilling problem from TSP
drill <- read_TSPLIB(system.file("examples/d493.tsp", package = "TSP"))
drill
tour <- solve_TSP(drill, method = "nn", two_opt = TRUE)
tour
plot(drill, tour, cex=.6, col = "red", pch= 3, main = "TSPLIB: d493")


## Write and read data in TSPLIB format
x <- data.frame(x=runif(5), y=runif(5))

## create TSP, ATSP and ETSP (2D)
tsp <- TSP(dist(x))
atsp <- ATSP(dist(x))
etsp <- ETSP(x[,1:2])

write_TSPLIB(tsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

write_TSPLIB(atsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

write_TSPLIB(etsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

## clean up
unlink("example.tsp")
## Drilling problem from TSP
drill <- read_TSPLIB(system.file("examples/d493.tsp", package = "TSP"))
drill
tour <- solve_TSP(drill, method = "nn", two_opt = TRUE)
tour
plot(drill, tour, cex=.6, col = "red", pch= 3, main = "TSPLIB: d493")


## Write and read data in TSPLIB format
x <- data.frame(x=runif(5), y=runif(5))

## create TSP, ATSP and ETSP (2D)
tsp <- TSP(dist(x))
atsp <- ATSP(dist(x))
etsp <- ETSP(x[,1:2])

write_TSPLIB(tsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

write_TSPLIB(atsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

write_TSPLIB(etsp, file="example.tsp")
#file.show("example.tsp")
r <- read_TSPLIB("example.tsp")
r

## clean up
unlink("example.tsp")

USCA312/USCA50 – 312/50 cities in the US and Canada

Description

The USCA312 dataset contains the distances between 312 cities in the US and Canada as an object of class TSP. USCA50 is a subset of USCA312 containing only the first 50 cities.

Format

USCA312 and USCA50 are objects of class TSP. USCA312_GPS is a data.frame with city name, long and lat.

Details

The USCA312_GPS dataset contains the location (long/lat) of the 312 cities.

Author(s)

Michael Hahsler

Source

John Burkardt, CITIES – City Distance Datasets, Florida State University, Department of Scientific Computing

Examples

data("USCA312")

## calculate a tour
tour <- solve_TSP(USCA312)
tour

# Visualize the tour if package maps is installed
if(require("maps")) {

  library(maps)
  data("USCA312_GPS")
  head(USCA312_GPS)

  plot((USCA312_GPS[, c("long", "lat")]), cex = .3)
  map("world", col = "gray", add = TRUE)
  polygon(USCA312_GPS[, c("long", "lat")][tour,], border = "red")
}
data("USCA312")

## calculate a tour
tour <- solve_TSP(USCA312)
tour

# Visualize the tour if package maps is installed
if(require("maps")) {

  library(maps)
  data("USCA312_GPS")
  head(USCA312_GPS)

  plot((USCA312_GPS[, c("long", "lat")]), cex = .3)
  map("world", col = "gray", add = TRUE)
  polygon(USCA312_GPS[, c("long", "lat")][tour,], border = "red")
}

Package 'TSP'

Help Index

Class ATSP – Asymmetric traveling salesperson problem

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Using the Concorde TSP Solver

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Cut a tour to form a path

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Class ETSP – Euclidean traveling salesperson problem

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Insert dummy cities into a distance matrix

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Reformulate a ATSP as a symmetric TSP

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

TSP solver interface

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Class TOUR – Solution to a traveling salesperson problem

Description

Usage

Arguments

Details

Author(s)

See Also

Examples

Calculate the length of a tour

Description

Usage

Arguments