A reverse logistics inventory model for plastic bottles
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present an original model for the production-recycling-reuse of plastic beverage bottles. Design/methodology/approach – It is assumed that discarded two-liter plastic polyethylene terephthalate (PET) bottles are collected from the market. The bottles are then sorted into non-contaminated and contaminated streams. The non-contaminated PET bottles are either remanufactured or used as regrind mixed with virgin PET to produce new bottles to satisfy varying demand. The contaminated bottles are either sold to industries using low-grade plastic or disposed of in a landfill. Numerical studies are used to illustrate the behaviour of the model, with an emphasis on exploring the reduction of total system cost and the amount of bottles going into a landfill. Findings – Numerical analyses conducted on the model found that the amount of bottles collected had the largest influence on the outcome of the total system unit time cost. Alternative materials to PET are surveyed and used to demonstrate a significant reduction in the cost of landfill disposal due to their more rapid degradation in the landfill. Research limitations/implications – Several areas for future work are highlighted. Potential modifications to the model could focus on accommodating bottles made of material other than plastic, incorporating the effects of learning on manual tasks, and on accommodating shortages or excess inventory. Originality/value – The model incorporates several unique aspects, including accounting for the cost of land use and associated environmental damage through the calculation of a present value that is charged to the manufacturer.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".