Here we collect FLO algorithms for solving different types of location problems.

Citation Policy: Please cite the corresponding articles if you are using implementations of FLO algorithms in scientific paper.

The algorithms will soon be available.

Alg. 1 : Rectangular Decomposition Algorithm

added: xx/08/2017  [MATLAB implementation]  [Informations]

Description: Computing the whole set of efficient solutions of a planar multi-objective location problem involving the Manhattan norm.

Keywords: Multiple objective programming, Location problem,  Manhattan norm, Scalarization, Nonessential objective

This implementation is based on theoretical results in:

• S. Alzorba, C. Günther, N. Popovici and Chr. Tammer : A new algorithm for solving planar multiobjective location problems involving the Manhattan norm, European Journal of Operational Research, Volume 258, Issue 1, pp 35-46, 2017 (DOI: 10.1016/j.ejor.2016.10.045)

Alg. 2 : Minimizing a quasi-concave function over the efficient set of a multi-objective location problem

added: xx/08/2017 [MATLAB implementation]  [Informations]

Description: Minimizing a quasi-concave objective function over the set of efficient solutions of a planar multi-objective location problem involving the Manhattan norm.

Keywords: Multicriteria optimization, Location problems, Decomposition, Pareto reducibility, Scalarization

This implementation is based on theoretical results in:

• S. Alzorba, C. Günther and N. Popovici : A special class of extended
  multicriteria location problems, Optimization, Volume 64, Issue 5, pp 1305-1320, 2015 (DOI: 10.1080/02331934.2013.869810)
• S. Alzorba, C. Günther, N. Popovici and Chr. Tammer : A new algorithm for solving planar multiobjective location problems involving the Manhattan norm, European Journal of Operational Research, Volume 258, Issue 1, pp 35-46, 2017 (DOI: 10.1016/j.ejor.2016.10.045)