A novel data-driven rolling horizon production planning approach for the plastic industry under the uncertainty of demand and recycling rate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficient production planning in the plastic industry requires integrating sustainability practices, particularly the use of recycled plastics. Recycling helps reduce environmental impacts and conserve resources by decreasing the reliance on virgin materials. To develop a responsive plan, dynamic approaches are beneficial to confront fluctuations in uncertain parameters such as recycling rate and market demand. The present study proposes a novel Data-driven Rolling Horizon Planning (DRHP) approach for a sustainable and dynamic production plan in the plastic industry. A dynamic Rolling Horizon (RH) planning framework is formulated as a multi-product, multi-period Mixed Integer Linear Programming (MILP) model for the problem. This model aims to minimize total production cost while taking into account the system’s constraints and sustainability considerations. To deal with uncertainty, the RH-based MILP model is coupled with a rolling Long Short-Term Memory (LSTM) model. The rolling LSTM model leverages historical data of market demand and recycling rate to predict their future values and consequently improve the responsiveness of the production plan. The outperformance of the proposed DRHP approach is demonstrated through an extensive comparison with a robust static counterpart in terms of total production cost and sustainability performance. Results indicate significant reductions, up to 80%, in production costs using the proposed DRHP approach. Furthermore, the effect of rolling duration is investigated concerning production cost, backlog, late-order, and inventory level. Findings highlight the potential of the proposed DRHP approach to mitigate inventory- and late-order-related challenges.
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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 | 0.000 |
| 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 it