Transport optimization of an anaerobic digestion co-product in a closed-loop supply chain
Bibliographic record
Abstract
This research explores the opportunities and challenges of integrated logistics, covering both the inbound (supply) and outbound (distribution) transportation to/from an anaerobic digestion plant involving several types of suppliers and customers in a region of Quebec (Canada). All participants supply organic residues (in liquid or solid form), and some are also customers who require to receive and use the co-product. The residues are transported to the plant by two types of truck (tanker and solid bulk) of different capacities, where they are transformed by anaerobic digestion. The resulting co-product, a digestate used as an organic fertilizer, must then be efficiently distributed to the customers. The main objective of this project is to size a fleet of trucks adapted to the needs and capacity of the plant under study and minimize transportation costs. After defining and modeling the problem by using mathematical optimization, several scenarios reflecting different transportation strategies (backhauling and heterogeneous truck fleet configuration) have been tested. This article presents and compares the results of the different scenarios, highlighting the economic benefits and suggesting future research avenues. The preliminary results show that substantial transportation cost savings can be obtained by using backhauling and a heterogeneous fleet, reaching up to 17% when both are combined while decreasing the traveled distance by up to 42%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".