A Sustainable Performance Assessment System for Road Freight Transport Based on Artificial Neural Networks
Bibliographic record
Abstract
The objective of this paper is to present a new multidimensional performance measurement model calculating the overall sustainable performance value applied to the road freight transport sector. The measurement system presented considers five main dimensions including economic, social, environmental, operational and stakeholder. This paper justifies the choice of these dimensions and details the calculation approach through the presentation of the different minimum conditions algorithms leading to the final global performance value. The model is then generalized here by means of the artificial neural network (ANN) which is found to be the most relevant modeling technique used in a variety of scientific domains. In this study, ANN is used to predict the value of the global multidimensional performance in road freight transport estimated following the machine learning of the program on a labeled database. The data on which the program trained emerged from our multidimensional performance measurement model. A model mainly designed for the sole purpose of quantifying the sustainable performance of a supply chain. To this end, we have identified five main dimensions recurrently cited in the literature, namely: economic, environmental, social, operational, and stakeholders. The dimensions' respective performances are obtained by employing a minimum condition algorithm, which returns the global multidimensional performance. The suggested model is general and may be applied to different disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".