Digitalization-driven circular economy in battery closed-loop supply chain network design
Bibliographic record
Abstract
Nowadays, Lithium-ion Batteries (LIBs) are growingly utilized in a wide range of products (e.g., electric vehicles) due to their superiority over all types of rechargeable batteries. The amount of valuable returned LIBs in various situations has increased dramatically as LIBs production has grown. As the collection and separation of returned LIBs have many challenges (e.g., identifying LIBs’ characteristics), using an Internet of Things (IoT)-based system could effectively address the problem of categorizing returned LIBs. This study aims to design the LIBs closed-loop supply chain to treat LIBs in different stages, including collection, separation, and recycling, to address the potential socio-environmental concerns. To do this, a multi-objective programming model is proposed to reduce environmental impacts and maximize social outcomes while minimizing total costs. Then, an integrated solution approach is developed encompassing four main phases: (i) Developing a digital transformation strategy to implement an IoT-based system, (ii) Employing a Partitioning Around Medoids (PAM)-Build algorithm to cluster the returned LIBs into repurposable returned LIBs, recyclable returned LIBs, and unrecyclable returned LIBs, (iii) Proposing an adaptive data-driven robust optimization to overcome the uncertainties of the studied problem, (iv) Developing an augmented epsilon-constraint method based on extracting efficient spaces. The results imply that increasing the rate of repurposable returned LIBs leads to worsening the values of the economic objective function (i.e., over 28%) and environment objective function (i.e., over 8%). Furthermore, transportation modes “Euro IV heavy-duty truck” and “Euro V heavy-duty truck” are more applicable in the proposed model since other ones are used when economic and environment-related objective functions are at their best values. • Proposing a framework for adopting digital transformation strategies in the battery industry. • Incorporating an IoT-based system into recycling centers within battery supply chains. • Addressing socioenvironmental issues in designing a battery closed-loop supply chain. • Developing a PAM-Build algorithm to cluster the quality of returned Lithium-ion batteries. • Specifying a trade-off surface of design objectives using an augmented epsilon constraint.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".