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Runtime-sensitive learned operator selection in ALNS: Testing improvements to adaptive operator selection while optimizing runtime

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  • Dudel, Christopher

Abstract

We propose and test two variations of the Adaptive Large Neighborhood Search (ALNS) meta-heuristic: First, we add time sensitivity to the operator selection scheme to optimize the ALNS for both solution quality and runtime. We reward comparatively slow operators with reduced rewards for finding improvements. This ensures that the meta-heuristic is slowed down less by operators which consistently find good solutions but take long to do so. Secondly, we replace the Adaptive Layer with aLearned Operator Selection Policy trained via Deep-Q Learning. The training takes both solution quality and operator runtime into account. We test our algorithms against classic ALNS as well as random operator selection. We perform an analysis of how operator portfolios affect performance. Our chosen problem domain is the Capacitated Vehicle Routing Problem with 100 to 400 customer nodes.

Suggested Citation

  • Dudel, Christopher, 2026. "Runtime-sensitive learned operator selection in ALNS: Testing improvements to adaptive operator selection while optimizing runtime," Junior Management Science (JUMS), Junior Management Science e. V., vol. 11(1), pages 1-26.
    • Handle: RePEc:zbw:jumsac:341444
    DOI: 10.5282/jums/v11i1pp1-26
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    References listed on IDEAS

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    1. Yossiri Adulyasak & Jean-François Cordeau & Raf Jans, 2014. "Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem," Transportation Science, INFORMS, vol. 48(1), pages 20-45, February.
    2. Alberto Santini & Stefan Ropke & Lars Magnus Hvattum, 2018. "A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic," Journal of Heuristics, Springer, vol. 24(5), pages 783-815, October.
    3. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
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