Modeling and Analysis of a Reverse Supply Chain Network for Lead-Acid Battery Manufacturing
Bibliographic record
Abstract
The North American lead-acid battery industry gains its environmental edge from its employment of closed-loop life cycle production. Nowadays, the typical new lead-acid battery contains 60 to 80 percent recycled lead and plastics. In this thesis, the closed-loop supply chain of a lead-acid battery manufacturing process has been investigated which extends the traditional supply chain to the entire product life cycle. A new tactical planning model has been developed for the entire closed-loop manufacturing process including purchasing, production, and end-of-life product return and recycling. The model is a multi-objective, multi-echelon mixed integer linear programming model, which minimizes the total costs and the total transportation pollution emissions, subject to structural and functional constraints. Decisions are made regarding material procurement, production, recycling and inventory levels, and the transportation modes between the echelons. Sensitivity analysis has been performed to evaluate the integration with third party outsourcing, changes in parameters and design options.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .Z435. Source: Masters Abstracts International, Volume: 45-01, page: 0440. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".