Enhancing efficiency in railway freight logistics using a two-stage decision support technique with Q-rung orthopair fuzzy sets
Bibliographic record
Abstract
Enhancing railway freight logistics efficiency is crucial for strengthening global supply chain performance, yet persistent challenges such as infrastructure limitations, operational inefficiencies, and fragmented intermodal integration hinder optimal performance. Despite its critical role in economic and environmental sustainability, limited research offers comprehensive, universally applicable solutions for addressing these issues. This study bridges this gap by introducing a novel multi-criteria decision-making framework that integrates inter-criteria correlation (CRiteria Importance Through Intercriteria Correlation (CRITIC)) and multi-objective optimization based on ratio analysis (Multi-attribute Multi-Objective Optimization based on Ratio Analysis (MULTIMOORA)) with Q-rung orthopair fuzzy sets (q-ROFSs) to handle complex and conflicting decision-making scenarios. These methods were selected for their complementary strengths. CRITIC effectively quantifies the importance of criteria by considering their interdependencies, MULTIMOORA offers robust multi-objective optimization capabilities, and q-ROFSs manage the inherent uncertainty and ambiguity of real-world logistics problems. Their integration provides a comprehensive framework capable of addressing both the complexity and uncertainty in railway freight logistics decision-making. A two-phase sensitivity analysis validates the framework’s reliability and consistency. Results indicate that “infrastructure investment” ranks as the most impactful strategy, followed by “intermodal transportation”. These findings offer practical guidance for policymakers and industry leaders, providing actionable solutions to enhance operational performance and sustainability while advancing the theoretical discourse in transportation logistics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".