An approximation algorithm for multiobjective mixed-integer convex optimization

Ina Lammel (), Karl-Heinz Küfer () and Philipp Süss ()
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Ina Lammel: Fraunhofer Institute for Industrial Mathematics (ITWM)
Karl-Heinz Küfer: Fraunhofer Institute for Industrial Mathematics (ITWM)
Philipp Süss: Fraunhofer Institute for Industrial Mathematics (ITWM)

Mathematical Methods of Operations Research, 2024, vol. 100, issue 1, No 12, 350 pages

Abstract: Abstract In this article we introduce an algorithm that approximates the nondominated sets of multiobjective mixed-integer convex optimization problems. The algorithm constructs an inner and outer approximation of the front exploiting the convexity of the patches for problems with an arbitrary number of criteria. In the algorithm, the problem is decomposed into patches, which are multiobjective convex problems, by fixing the integer assignments. The patch problems are solved using (simplicial) Sandwiching. We identify parts of patches that are dominated by other patches and ensure that these patch parts are not refined further. We prove that the algorithm converges and show a bound on the reduction of the approximation error in the course of the algorithm. We illustrate the behaviour of our algorithm using some numerical examples and compare its performance to an algorithm from literature.

Keywords: Multiobjective optimization; Mixed-integer optimization; Approximation algorithm; Convex optimization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00186-024-00870-3

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