Matheuristic approach and a mixed-integer linear programming model for biomass supply chain optimization with demand selection
Bibliographic record
Abstract
It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".