An adaptive simulation-based inventory optimisation approach to enhancing biomass supply chain resilience: a case study of remote communities
Bibliographic record
Abstract
Bioenergy conversion presents a promising pathway for enhancing energy security and promoting the circular economy. However, biomass supply chains (BSCs) are inherently stochastic and vulnerable to supply-demand imbalances and operational disruptions. Therefore, effective inventory management is essential for improving the reliability of BSCs, as it affects material flow and network performance. This study proposes a hybrid approach that integrates simulation modelling with optimisation techniques to address inventory-related decisions in multi-echelon BSCs. This framework captures the dynamic interplay among intertwined network agents and explores the interaction between energy generation and operational decisions while accounting for uncertainty factors. Through a case study of remote, off-grid communities in Quebec, the impact of balancing inventory under dual sourcing strategies and transportation time windows on system reliability, cost, and CO2 emissions is examined. The findings indicate that integrating biomass into the energy network can reduce electricity unit costs by almost 14% and cut CO2 emissions by 49% compared to a diesel-only scenario. Adaptive inventory monitoring intervals further improve system performance, as shorter periods increase bioenergy share at demand points by 11% and reduce emissions by 10%. The model helps decision-makers balance trade-offs among cost, resilience, and sustainability objectives when planning biomass-based energy systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".