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Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization

under review
  • We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context before, a systematic methodology and analysis remain lacking. We address this gap by providing a theoretical characterization of warm-starting within scalarization methods, focusing on the sequencing of subproblems. However, optimizing the order of subproblems to maximize warm-start efficiency may conflict with alternative criteria, such as early identification of infeasible regions. We quantify these trade-offs through an extensive computational study.
Metadaten
Author:Stephanie RiedmüllerORCiD, Janina ZittelORCiD, Thorsten KochORCiD
Document Type:In Proceedings
Parent Title (English):Operations Research Proceedings 2025
Year of first publication:2025
ArXiv Id:http://arxiv.org/abs/2507.21933
Preprint:urn:nbn:de:0297-zib-101073
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