TravelingSalesmanExact

This is a simple Julia package to solve the traveling salesman problem using an Dantzig-Fulkerson-Johnson algorithm. I learned about this kind of algorithm from the very nice blog post http://opensourc.es/blog/mip-tsp which also has a Julia implementation. In the symmetric case, the implementation in this package uses the symmetry of the problem to reduce the number of variables, and is similar to the "reduced clustering" version of the algorithms described by (Pferschy and Staněk, 2017), using TravelingSalesmanHeuristics to warmstart the solves with good solutions, as well as splitting the problem into subproblems using Clustering.jl to obtain some good subtour elimination constraints.

While TravelingSalesmanExact is functional and somewhat practical for small problems, in 2003, Concorde already could solve much larger problems faster! So we are far away from the state of the art here. See Performance below for some more details.

See also TravelingSalesmanHeuristics.jl for a Julia implementation of heuristic solutions to the TSP (which will be much more performant, especially for large problems, although not exact). Additionally, see TravelingSalesmanBenchmarks for one use of this package: generating exact cost values for test-cases to help tune the heuristics of the aforementioned TravelingSalesmanHeuristics.jl.

Generating subtour elimination constraints for the TSP from pure integer solutions Pferschy, U. & Staněk, R. Cent Eur J Oper Res (2017) 25: 231. https://doi.org/10.1007/s10100-016-0437-8

Solution of a Large-Scale Traveling-Salesman Problem G. Dantzig, R. Fulkerson, and S. Johnson, J. Oper. Res. Soc. (1954) 2:4, 393-410 https://doi.org/10.1287/opre.2.4.393

Requires Julia (https://julialang.org/downloads/).

This package is registered, so you can add it via

] add TravelingSalesmanExact

You also need a mixed-integer solver to do the underlying optimization. For example, SCIP is a free, open-source solver (see https://github.com/scipopt/SCIP.jl for the compatible Julia wrapper) and can be installed by

] add SCIP

Gurobi is a commercial wrapper that offers free academic licenses. It has a compatible Julia wrapper Gurobi (https://github.com/JuliaOpt/Gurobi.jl) that can be installed via

] add Gurobi

Note you also need Gurobi itself installed and a license properly configured.

See ./tsplib for some benchmarking results on small problems from TSPLIB95.

With SCIP:

using TravelingSalesmanExact, SCIP
set_default_optimizer!(optimizer_with_attributes(SCIP.Optimizer, "limits/maxorigsol" => 100))
n = 50
cities = [ 100*rand(2) for _ in 1:n];
tour, cost = get_optimal_tour(cities; verbose = true)
plot_cities(cities[tour])

To use Gurobi, the first few lines can be changed to:

using TravelingSalesmanExact, Gurobi
const GurobiEnv = Gurobi.Env()
set_default_optimizer!(() -> Gurobi.Optimizer(GurobiEnv, OutputFlag = 0))

Note that without the OutputFlag = 0 argument, Gurobi will print a lot of information about each iteration of the solve.

One can also pass an optimizer to get_optimal_tour instead of setting the default for the session, e.g.

using TravelingSalesmanExact, SCIP
n = 500
cities = [ 100*rand(2) for _ in 1:n];
tour, cost = get_optimal_tour(cities, SCIP.Optimizer; verbose = true)
plot_cities(cities[tour])