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Multi-objective unconstrained combinatorial optimization: a polynomial bound on the number of extreme supported solutions

  • Britta SchulzeEmail author
  • Kathrin Klamroth
  • Michael Stiglmayr
Article
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Abstract

The multi-objective unconstrained combinatorial optimization problem (MUCO) can be considered as an archetype of a discrete linear multi-objective optimization problem. It can be interpreted as a specific relaxation of any multi-objective combinatorial optimization problem with linear sum objective function. While its single criteria analogon is analytically solvable, MUCO shares the computational complexity issues of most multi-objective combinatorial optimization problems: intractability and NP-hardness of the \(\varepsilon \)-constraint scalarizations. In this article interrelations between the supported points of a MUCO problem, arrangements of hyperplanes and a weight space decomposition, and zonotopes are presented. Based on these interrelations and a result by Zaslavsky on the number of faces in an arrangement of hyperplanes, a polynomial bound on the number of extreme supported solutions can be derived, leading to an exact polynomial time algorithm to find all extreme supported solutions. It is shown how this algorithm can be incorporated into a solution approach for multi-objective knapsack problems.

Keywords

Multi-objective combinatorial optimization Multi-objective unconstrained optimization Weight space decomposition Arrangement of hyperplanes Zonotopes Knapsack problem 

Notes

Acknowledgements

This work was partially supported by the bilateral cooperation project Multiobjective Combinatorial Optimization: Beyond the Biobjective Case funded by the Deutscher Akademischer Austausch Dienst (DAAD, Project-ID 57212018).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of WuppertalWuppertalGermany

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