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An Online Non-Stationary Simulation Optimization Approach Based on Regime Switching — arXiv:2508.12634

Source: arXiv (en)
Original: https://arxiv.org/abs/2508.12634
License (as archived): CC0-1.0
Archived: 2026-10-07T23:02:46.241Z
SHA-256 of the archived text: fda1000602680c3101dcd80f68c872569762b8d5ca660c7fc3eb902c847bb17a
Stable URL of this page: https://metraipolis.com/documento/dc_dec47ddbc336

How to cite

An Online Non-Stationary Simulation Optimization Approach Based on Regime Switching — arXiv:2508.12634 — arXiv (en), https://arxiv.org/abs/2508.12634 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dec47ddbc336 (Metraipolis archive dc_dec47ddbc336, sha256 fda1000602680c3101dcd80f68c872569762b8d5ca660c7fc3eb902c847bb17a)

BibTeX

@misc{dcdec47ddbc336,
  title = {An Online Non-Stationary Simulation Optimization Approach Based on Regime Switching — arXiv:2508.12634},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2508.12634},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dec47ddbc336; sha256 fda1000602680c3101dcd80f68c872569762b8d5ca660c7fc3eb902c847bb17a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2508.12634. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Dynamic and evolving operational and economic environments present significant challenges for decision-making. We explore a simulation optimization problem characterized by non-stationary input distributions with regime-switching dynamics across sequential decision stages. This problem encompasses both prediction uncertainty, arising from the regime-switching behavior of input distributions, and input uncertainty, resulting from parameter estimation for these distributions and their dynamics using finite data streams. To address these uncertainties, we develop a Bayesian framework that approximates the true objective function using a Markov Switching Model (MSM). We rigorously validate this approximation by establishing the consistency and asymptotic normality of the objective functions and optimal solutions. To solve the problem in an online fashion, we propose a metamodel-based algorithm that leverages simulation results from previous stages to enhance decision-making. Furthermore, we tackle scenarios with an unknown number of regimes through a Bayesian nonparametric method. Numerical experiments demonstrate that our algorithm achieves superior performance and robust adaptability.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in stat.ML).
Categorie arXiv: Optimization and Control (math.OC) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2508.12634 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:41:13.184Z dalla pagina https://arxiv.org/catchup/stat.ML/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).