A multi-objective optimization approach for sustainable management of computers E-waste in a closed-loop supply chain network
Bibliographic record
Abstract
This study explores the integration of reverse and forward supply chains in a Closed-Loop Supply Chain (CLSC) for electronic waste management , driven by business and government regulatory concerns. It highlights the economic benefits and efficient management of Electronic Waste (E-waste), particularly focusing on End-of-Life (EoL) products to tackle the global E-waste crisis. This research introduces a novel multi-objective mixed-integer linear programming model adapted for an E-waste CLSC network. This model incorporates hybrid manufacturing facilities and Triple Bottom Line (TBL) objectives to maximize profits and social innovations while minimizing gas emissions to reduce landfill waste. An application involving a computer manufacturing network in Ontario , Canada, utilizing Google Maps for distance calculations, illustrates the design and optimization impact of an electronic CLSC network. This study employs computational experiments and sensitivity analyses, using three solution methods, including weighted-sum, ε -constraint, and hybrid approaches within a multiperiod framework to validate the model's robustness. These methods help decision-makers integrate a TBL approach into the CLSC network which reflects economic, environmental, and social factors. By generating Pareto optimal solutions using these methods, decision-makers can evaluate different options through trade-off analysis. The findings show that the ε -constraint method offers a greater number of efficient solutions, enabling a better balance of objectives. Finally, this study concludes with managerial insights and recommendations based on the research outcomes. The findings provide insights into product and part flows, facilities utilization, and distribution across the network segments.
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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 | 0.001 | 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".