Application of IoT technology for enhancing the consumer willingness to return E-waste for achieving circular economy: A Lagrangian relaxation approach
Bibliographic record
Abstract
Poor management of waste electronic and electrical equipment (e-waste) has resulted in serious challenges to the environment and, consequently, to living organisms. Increasing consumer willingness to return household e-waste is a practical solution for better e-waste management. In this paper, for the first time, a circular supply chain network is structured by applying internet of things (IoT) technology to increase consumer willingness to return household e-waste and to manage the network more effectively. For this purpose, we formulate a mixed-integer linear programming model whose objective is to minimize strategic costs (setup costs and IoT deployment costs) and operational costs (transportation costs, costs associated with IoT, and processing costs in centers), simultaneously. In addition, we developed a heuristic algorithm based on Lagrangian relaxation to solve the proposed MILP model. The performance of the proposed algorithm is evaluated by comparing its results with GAMS results using eight small-sized simulated problems. Furthermore, eight large-size problems are simulated and solved by the proposed algorithm. Finally, the data of an Iranian knowledge-based company is employed to validate the presented mathematical model and algorithm, and the sensitivity analysis process is used to examine the accuracy of the obtained results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".