Tire Closed-loop Supply Chain Network Design Considering Multiple Objectives and Regulatory Requirements
Bibliographic record
Abstract
For tire manufacturers and related businesses to comply with the resource recovery laws and regulatory frameworks, the opportunity lies in shifting to Closed-Loop Supply Chain (CLSC) which integrates both forward and reverse supply chains. In this research, a new mathematical model is proposed to configure and optimize a tire CLSC network. The optimal quantities for the flows of products, and number and locations of open facilities of the network are calculated by solving the model. The extension of a multi-objective model is done by considering four new objective functions about suppliers. Then, a novel decision-making method based on Spherical fuzzy logicisdeveloped for theweightsofsuppliers. Finally,themodelissolvedbytheaugmented ε-constraint method. The application of the model is illustrated focusing on the Greater Toronto Area (GTA) in Ontario, Canada. The results show that the weights of the returned products are the critical parameters for the compliance of government regulation. Besides, considering multiple objectives for supplier selection in the tire CLSC network design andoptimizationmay have effect on both the selected suppliers and the allocated orders.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".