This is a simple Julia package to solve the travelling saleman 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 essentially is the most basic version of the algorithms described by (Pferschy and Staněk, 2017) (i.e. no warmstarts or clustering methods for subtour elimination as a presolve step).
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
Generating subtour elimination constraints for the TSP from pure integer solutions
Pferschy, U. & Staněk, R. Cent Eur J Oper Res (2017) 25: 231.
Solution of a Large-Scale Traveling-Salesman Problem
G. Dantzig, R. Fulkerson, and S. Johnson, J. Oper. Res. Soc. (1954) 2:4, 393-410
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 compatible with JuMP 19+ to do the underlying optimization. For example,
GLPK is a free, open-source solver (see https://github.com/JuliaOpt/GLPK.jl for the compatible Julia wrapper) and can be installed by
] add GLPK
Gurobi is a commerical wrapper that offers free academic licenses. It has a compatible Julia wrapper
that can be installed via
] add Gurobi
Note you also need Gurobi itself installed and a license properly configured.
using TravelingSalesmanExact, GLPK set_default_optimizer!(with_optimizer(GLPK.Optimizer)) 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!(with_optimizer(Gurobi.Optimizer, GurobiEnv, OutputFlag = 0))
Note that without the
OutputFlag = 0 keyword argument to the
with_optimizer call, Gurobi will print a lot of information about each iteration of the solve.
Mosek is another commerical wrapper that offers free academic licenses. It has a compatible Julia wrapper
MosekTools (https://github.com/JuliaOpt/MosekTools.jl). You also need a license properly configured; the older wrapper Mosek.jl offers instructions for this.
Mosek can be used as e.g.
using TravelingSalesmanExact, MosekTools set_default_optimizer!(with_optimizer(Mosek.Optimizer, QUIET = true))
One can also pass an optimizer to
get_optimal_tour instead of setting the default for the session, e.g.
using TravelingSalesmanExact, GLPK n = 50 cities = [ 100*rand(2) for _ in 1:n]; tour, cost = get_optimal_tour(cities, with_optimizer(GLPK.Optimizer); verbose = true) plot_cities(cities[tour])
See https://ericphanson.github.io/TravelingSalesmanBenchmarks.jl/html/random_50_cities_stats.html for a benchmark comparing the computation time between these solvers on random problems as well as comparing to that of heuristics.