Universal Optimization Framework (UOF)

The Universal Optimization Framework (UOF) Is a research-oriented and methodology-generating framework for systems-level characterization of operational efficiency, loss reduction, traceability and sustainability. It is intended as a structured input to management systems, organizational decision processes, institutional pilots and international standardization. UOF provides a horizontal decision-support layer across four domains: efficiency, adverse loss, traceability and sustainability. Every observable is normalized in a favorable direction, while the loss component is converted once to adverse orientation and then complemented once in the system score. The framework can begin with a controlled spreadsheet and scale to APIs, streaming evidence, digital threads, QHD-enabled lifecycle assurance, simulation and advisory AI. It does not convert weak data into strong evidence, and it does not authorize an irreversible action merely because a score or model output is favorable.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-09
DOI
https://doi.org/10.5281/zenodo.22675810
Primary Topic
Transportation Systems and Infrastructure
Type
article
Field-Weighted Citation Impact
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article

Universal Optimization Framework (UOF)

Carlos Daniel Borrego Romero
Zenodo (CERN European Organization for Nuclear Research)
Transportation Systems and Infrastructure
article

Universal Optimization Framework (UOF)

Carlos Daniel Borrego Romero
article en

Abstract

The Universal Optimization Framework (UOF) Is a research-oriented and methodology-generating framework for systems-level characterization of operational efficiency, loss reduction, traceability and sustainability. It is intended as a structured input to management systems, organizational decision processes, institutional pilots and international standardization. UOF provides a horizontal decision-support layer across four domains: efficiency, adverse loss, traceability and sustainability. Every observable is normalized in a favorable direction, while the loss component is converted once to adverse orientation and then complemented once in the system score. The framework can begin with a controlled spreadsheet and scale to APIs, streaming evidence, digital threads, QHD-enabled lifecycle assurance, simulation and advisory AI. It does not convert weak data into strong evidence, and it does not authorize an irreversible action merely because a score or model output is favorable.

Zenodo (CERN European Organization for Nuclear Research)
Responsible consumption and production
Openalex Percentile: Top 5%
Transportation Systems and Infrastructure
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