Ring Star Problem variants Solver
The references:
Introduce the Resilient Ring Star Problem (called 1-R-RSP).
The package can solve 1-R-RSP thanks to:
- A Branch-and-Benders-cut algorithm (referred to as B&BC)
- An Integer Linear Programming model (ILP)
When setting backup_factor=0 or tildeV=0, 1-R-RSP reduces to RSP
A Survivable Ring Star Problem solver will be added.
JuMP.jl must be installed. You can use CPLEX, GLPK, Gurobi, Xpress or any solver that supports Lazy Constraints callback in JuMP.
julia> import Pkg ; Pkg.add("RingStarProblems")julia> import RingStarProblems as RSP
julia> using JuMP
julia> pars = RSP.SolverParameters(
solve_mod = RSP.BranchBendersCut(), # ILP() or BranchBendersCut()
F = 7, # total failing time F in days per year, see [`PhD manuscript`](https://theses.hal.science/tel-04319443)
o_i = 0, # opening costs
s_ij = RSP.Euclidian(), # star costs
r_ij = RSP.Euclidian(), # ring costs
backup_factor = 0.01, # backup_factor c'=0.01c and d'=0.01c
alpha = 3, # See [`Labbรฉ et al., 2004`](ttps://doi.org/10.1002/net.10114)
tildeV = 100, # uncertain nodes set
writeresults = RSP.WHTML(), # output results locally, WLocal(), WHTML() or no output false
plotting = false, # plot results to subfolder src/plots/results/
log_level = 1, # console output log_level
redirect_stdio = false, # redirecting_stdio to output file
)To use GLPK optimizer:
julia> using GLPK
julia> symbolinstance = :TinyInstance_12_2
julia> RSP.rspoptimize(pars, symbolinstance, optimizer_with_attributes(GLPK.Optimizer,
"msg_lev" => GLPK.GLP_MSG_ALL
))To use Gurobi optimizer:
julia> using Gurobi
julia> symbolinstance = :berlin52
julia> RSP.rspoptimize(pars, symbolinstance, Gurobi.Optimizer)Either:
julia> x = 1:10
julia> y = rand(1:10, 10)
julia> RSP.rspoptimize(pars, x, y, Gurobi.Optimizer)Or:
julia> xycoors = tuple.(1:10, rand(1:10, 10))
julia> RSP.rspoptimize(pars, xycoors, Gurobi.Optimizer)To plot the solutions in the folder ext/results
julia> using GraphPlot, Compose, Colors
julia> pars.plotting = trueThen call rspoptimize again