Collection network design with capacity planning in reverse logistics: static and restricted-dynamic models
Why this work is in the frame
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Bibliographic record
Abstract
This study proposes two collection network design models that determine the locations and capacities of collection centres and the allocations of refuse at demand points to the opened collection centres: a single-period static model for time-invariant demands and a multi-period restricted-dynamic model for time-variant demands over a planning horizon. The capacities of collection centres are not given, but decision variables are used to obtain cost savings by minimizing surplus capacities. The maximum allowable distance between collection centres and demand points and the minimum recovery rates of collection centres are also considered. Two heuristics are proposed for each of the two problems after formulating them as integer programming models. Computational experiments were conducted on various test instances, and the results are reported. It is shown from the test results that the restricted-dynamic approach outperforms the static model significantly when the refuse demands are time variant. Finally, some managerial insights are derived.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it