Applications of machine learning for decision support in biomass supply chains: A systematic review
Bibliographic record
Abstract
• Reviewed Machine Learning (ML) applications in biomass supply chain (BSC). • Used scientific mapping for bibliographic coupling and keyword analysis. • Identified key ML challenges: data limits, scalability, and generalization. • Highlighted ML potential to enhance planning in biomass supply chains. • There have been limited work on ML and optimization model integration in BSCs. Effective planning of biomass supply chains (BSC), which involve collection, transportation, pre-processing, storage, conversion, and delivery of bioproducts, is essential to ensure efficiency and sustainability. Recently, machine learning (ML) has been adopted to address the supply chain’s complexities for effective planning. ML provides dynamic and data-driven solutions that enhance decision-making. It has been applied for predicting biomass yields, forecasting supply and demand, optimizing logistics and facility location, and improving the efficiency of conversion processes. This review paper highlights the role of ML in BSC planning. This study considers biomass sources such as food processing residues, animal waste (e.g., manure), in addition to forest-based and agricultural-based biomass, examining processes across all stages of a supply chain from upstream to downstream. We examine ML models in previous studies based on their learning paradigms: supervised, unsupervised, and reinforcement learning, and the type of performed analytics: predictive, and both predictive and prescriptive analytics. Challenges related to data availability, computational requirements, and model generalization limit ML applications in BSCs. Future research could focus on scalable and adaptable models for preprocessing, transportation, and harvesting activities by addressing the uncertainty. Integrating advanced ML could significantly enhance the resiliency, sustainability, and efficiency of BSCs, supporting bioeconomy advancement and the achievement of sustainability goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".