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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23858 |
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| _version_ | 1866911473708564480 |
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| author | Markhorst, Berend Zocca, Alessandro Berkhout, Joost van der Mei, Rob |
| author_facet | Markhorst, Berend Zocca, Alessandro Berkhout, Joost van der Mei, Rob |
| contents | Network design under uncertainty arises in countless real-world settings and can be captured by the Stochastic Steiner Tree Problem (SSTP). Although there are a few approaches specifically tailored to this stochastic optimization problem, there are considerably more state-of-the-art heuristics for its deterministic variant, the Steiner Tree Problem (STP). In this work, we show how to leverage an existing STP heuristic in building a novel method for solving its stochastic variant, the SSTP. This approach is a powerful, yet simple and easy-to-implement way of solving this complex problem. We test our method using benchmark instances from the literature. Numerical results show considerably faster computation times compared to the state-of-the-art, with a gap of approximately 5%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23858 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Fast Heuristic for Stochastic Steiner Tree Problems Markhorst, Berend Zocca, Alessandro Berkhout, Joost van der Mei, Rob Optimization and Control Network design under uncertainty arises in countless real-world settings and can be captured by the Stochastic Steiner Tree Problem (SSTP). Although there are a few approaches specifically tailored to this stochastic optimization problem, there are considerably more state-of-the-art heuristics for its deterministic variant, the Steiner Tree Problem (STP). In this work, we show how to leverage an existing STP heuristic in building a novel method for solving its stochastic variant, the SSTP. This approach is a powerful, yet simple and easy-to-implement way of solving this complex problem. We test our method using benchmark instances from the literature. Numerical results show considerably faster computation times compared to the state-of-the-art, with a gap of approximately 5%. |
| title | A Fast Heuristic for Stochastic Steiner Tree Problems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2602.23858 |