Avalilação PREreview Estruturada de Towards explainable decision support using hybrid neural models for logistic terminal automation

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction clearly states what problem the paper is addressing (loss of explainability and causal reliability in deep learning for logistics), why that problem matters for decision support, and what the authors aim to do (propose an interpretable-by-design hybrid neural system dynamics framework). The objective is explicit and easy to understand without having to infer it later from the methods or results.

Are the methods well-suited for this research?

Highly appropriate

The methods are a strong fit for the research goal. The paper is not trying to optimize prediction alone, it is explicitly about explainable, causally grounded decision support in logistics. Using System Dynamics as the structural backbone, combined with deep learning, concept-based interpretability, and causal machine learning, directly matches that objective. The approach follows accepted best practices for hybrid and interpretable modeling and is well aligned with the problem domain. Nothing in the methods appears ad hoc or misaligned with the research question. While empirical validation could always be expanded, that affects depth, not suitability.

Are the conclusions supported by the data?

Somewhat supported

The conclusions are reasonable and consistent with what the authors present, especially given that this is a framework and methodology paper, not a results-heavy empirical study. The claims about improved interpretability, causal grounding, and decision-support relevance are supported by the conceptual design and the AutoMoTIF context. However, the paper does not yet provide extensive quantitative evidence or broad experimental validation that would fully justify a “highly supported” rating. Some conclusions are necessarily forward-looking and based on demonstrated feasibility rather than strong empirical comparison across datasets or baselines.

Are the data presentations, including visualizations, well-suited to represent the data?

Somewhat appropriate and clear

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Somewhat clearly

Is the preprint likely to advance academic knowledge?

Somewhat likely

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, but it needs to be improved

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.