VOptGeneric.jl

Solver of multiobjective linear optimization problems (MOMIP, MOLP, MOIP, MOCO): generic part
Author vOptSolver
Popularity
9 Stars
Updated Last
1 Year Ago
Started In
June 2017

vOptGeneric: part of vOptSolver for non-structured problems

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vOptSolver is a solver of multiobjective linear optimization problems (MOMIP, MOLP, MOIP, MOCO). This repository concerns vOptGeneric, the part of vOptSolver devoted to multiobjective non-structured problems (currently available: MOIP). With vOptGeneric, the problem is expressed using JuMP algebraic language extended to multiple objectives. vOptGeneric runs on macOS, linux-ubuntu, and windows.

We suppose you are familiar with vOptSolver; if not, read first this presentation.

Instructions

For a local use, a working version of:

  • Julia must be ready; instructions for the installation are available here
  • your favorite MIP solver must be ready (GLPK is suggested); instructions for the installation are available here

Run Julia

On linux:

  • open a console on your computer or in the cloud
  • when the prompt is ready, type in the console julia

On macOS:

  • locate the application julia and
  • click on the icon, the julia console comes to the screen

Installation Instructions

Before your first use,

  1. run Julia and when the terminal is ready with the prompt julia on screen,
  2. add as follow the mandatory packages to your Julia distribution:
julia> using Pkg
julia> Pkg.add("vOptGeneric")
julia> Pkg.add("GLPK")

That's all folk; at this point, vOptGeneric is properly installed.

Usage Instructions

When vOptGeneric is properly installed,

  1. run Julia and when the terminal is ready with the prompt julia on screen,
  2. invoke vOptGeneric and the MILP solver to activate in typing in the console:
julia> using vOptGeneric
julia> using GLPK

vOptGeneric is ready. See examples for further informations and have fun with the solver!

Problems available

Problem Description Output Method Parameter (if required) Name
2-IP bi-objective Integer Linear Program Y_N :epsilon step = realValue ϵ-constraint
2-IP bi-objective Integer Linear Program Y_N :chalmet or :Chalmet step = realValue Chalmet
2-IP bi-objective Integer Linear Program Y_{SN} :dicho or :dichotomy (none) Aneja & Nair
p-IP multi-objective Integer Linear Program Y_{lex} :lex or :lexico (none) Lexicographic

Examples

The folder examples provides (1) source code of problems ready to be solved and (2) selected datafiles into different formats.

Limitations

No special limitation; the solving strength of vOptGeneric is currently provided by the MIP solver (GLPK, Clp/Cbc, Cbc, GUROBI, etc.) invoked.