The prototype version of the MOP solver, named vOptSolver, is under development. It is a free open source MOLP/MOMIP/MOCO/MOIP solver available under the MIT licence. It can be used as an open box for the scientific community, as well as a black box for the practitionners, through a collection of API.
The version 0.0.1 implemented in C/C++ languages and runs under the operating systems linux has been released in July 2016. It integrates the following methods and algorithms:
- a parametric bi-objective simplex algorithm
- the bi-objective linear assigment algorithm [1,4]
- the bi- and three- objective 01 unidimensional knapsack algorithm 
- the bi-objective discrete uncapacited facility location algorithm 
The version 0.0.2 has been released as open source in June 2017. It is now implemented in Julia language and integrates algorithms implemented in Julia/C/C++ languages. Based on JuMP, vOptSolver provides a modeling language supporting the following classes of multi-objective optimization problems: MOLP, MOMILP, MOCO, MOIP.
The last version of vOptSolver is downloadable here:
http://vOptSolver.github.io/vOptSolver (homepage of vOptSolver)
http://github.com/vOptSolver/vOptSpecific.jl (repository of vOptSpecific)
http://github.com/vOptSolver/vOptGeneric.jl (repository of vOptGeneric)
Follow the video (in French) presented during the GDR RO tutorial talk at ROADEF’2018 conference:
References of the algorithms integrated:
 A. Przybylski, X. Gandibleux, M. Ehrgott: Two phase algorithms for the bi-objective assignment problem. European Journal of Operational Research, Volume 185, Issue 2, Pages 509-533, 2008.
 J. Jorge: Nouvelles propositions pour la résolution exacte du sac à dos multi-objectif unidimensionnel en variables binaires. PhD Thesis, Université de Nantes, 2010.
 X. Gandibleux, A. Przybylski , S. Bourougaa, A. Derrien, A. Grimault: Computing the Efficient Frontier for the 0/1 Biobjective Uncapacitated Facility Location Problem CORS/MOPGP’2012 (10th international conference on Multiple Objective Programming and Goal Programming). June 11-13, 2012, Niagara Falls, Canada.
 X. Gandibleux, H. Morita, N. Katoh. A population-based algorithm for solving linear assignment problems with two objectives. Computers & Operations Research. In Press, Available online, 2016.