Evaluation of Mathematical Models in Sustainable Supply Chain Management: Gap Analysis
Bibliographic record
Abstract
The main purpose of this paper is to present a comprehensive view of the application mathematical models in the designing and implementing SSCM beside to solving problems and making decision. The research questions are: what kind of mathematical models are used for designing and implementing sustainable supply chain management, how to use them, which industries implemented in, what modules of SSCM depth in and finally finding the gaps between the goals of Sustainable development and current researches and suggestions for further researches. The methodology of the research is Systematic Literature review and evaluation peer review papers which are published in high ranking journals: First, we gather all papers through scientific data bases like Scopus, science direct, MDPI, Springer, Google Scholar. Then, screening papers based on the criteria such as object of paper, subject of paper, journals impact factor, peer review paper, and relative content of the papers. Finally, we selected 245 papers through three steps screening from 2806 papers that they have enough quality and relative to our research goals for context analysis. For context analysis: First we categorize the information of the papers and draw the current situation of researches in the framework of our topic. Then, we evaluate and compare the goals of sustainability and current situation and find the gapes, then, offer suggestions required researches for pollutant industries such as Casting Industry, Heavy Industry, Coal Industry and so on. On the other hand, there are gaps in researches in some modules of SSCM such as packaging, designing products, etc.
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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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".