Optimising agri-food supply chains: Managing food waste through harvest and side-stream valorisation
Bibliographic record
Abstract
The importance of reuse and valorisation as a means to enhance sustainable production practices, by reducing waste streams through the recovery of resources in the chain, is increasingly recognised. Confronted with new challenges in the context of climate change, the food and agricultural sector stands to benefit, in particular, from valorising (edible) side streams, such as unharvested crop parts and vegetable peels, that are often overlooked by consumers. Focusing on side-stream valorisation strategies within food processing facilities, this research develops a mixed-integer optimisation model to support decision-makers in determining an optimal product portfolio and processing configuration. This model is solved for two key performance indicators, considering both the economic and the environmental impact, in the form of total profit and exergy loss. Examining potential trade-offs between the two objectives, we present a real-life case study from a carrot processing company. We explore several scenarios and case settings to investigate the impact of various factors on the potential of side-stream valorisation. The findings from our analysis demonstrate that side-stream valorisation seems to be generally well aligned with profit maximisation, while it is not always beneficial from an environmental impact perspective. • Modelling food valorisation through supply chain network (re-)design. • Novel optimisation model incorporating flexible recipes and quality changes. • Insights obtained from a real-life case study of a carrot processor. • Analysing the impact of valorisation on profit and environmental impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".