Utilizing energy transition to drive sustainability in cold supply chains: a case study in the frozen food industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In alignment with the ever-growing interest in adopting sustainable practices, this paper devises a cold supply chain (CSC) planning model that integrates the three pillars of sustainability into the decision-making process while accounting for the shift towards clean energy sources. Interrelated decisions pertaining to production-distribution strategy, backorder and inventory levels, choice of truck type, and selection of third-party logistics (3PLs) providers are jointly optimized. For global CSCs in specific, such decisions are particularly sensitive to the energy sources of the refrigerated facilities and the accompanying levels of CO 2 emissions generated. As such, a multi-objective mixed-integer non-linear programming (MINLP) model is developed and then solved via the weighted-sum method. In essence, the model seeks to operationalize sustainability goals by considering the rapidly evolving transition in energy sources across different regions when deciding on which 3PLs to engage in a contractual agreement with while adjusting the production and distribution strategy accordingly. The practical relevance of the model is illustrated using a case study drawn from the North American frozen food industry. The conducted trade-off analysis indicates the possibility of obtaining a drastic improvement of 86% in jobs’ stability levels (social measure) with a maximum cost increase of around 9% as compared to the economic measure. Furthermore, the analysis reveals that it is possible to reduce 71% of CO 2 emissions while attaining 63% reduction in worker variations at the expense of only 4.47% cost increase once compared to solely optimizing the economic objective.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it