A production and distribution scheduling matheuristic for reducing supply chain variations
Bibliographic record
Abstract
In a supply chain network, satisfying the demand at the shop level while having a smooth production at the manufacturer (or plant) level are usually conflicting objectives. For instance, the production variations will be high if the shops can order exactly what they need and when they need it. On the other hand, producing the same amount each day prevents to adapt to the variations of the demand and may generate shortages or excess inventory. This study, performed in collaboration with a major fast-moving consumer goods company, proposes a lexicographic model for managing the supply chain in an integrated manner. Seven objective functions are considered to represent the goals of various stakeholders along the supply chain (from the shop to the plant) and different priority levels. A matheuristic combining both local-search procedures and exact methods is designed for scheduling the production orders and the shipments along the supply chain to optimize the overall cost structure. The proposed neighborhood structures employed in the local-search heuristics are able to perform dedicated modifications with respect to a single objective function (e.g., shortage, production variability, inventory level) and a single solution characteristic (e.g., production, shipment) without degrading the value of higher-level objectives. As computation time is limited, a time management approach for this method is proposed. Experiments are performed on 120 instances generated with the company to capture the real situations it faces. Using a rolling-window simulation with forecasted demands, we show that our method clearly outperforms a commercial solver and several common policies used in practice. The benefit of the proposed approach is highlighted both in terms of runtime and solution quality. • Real problem motivated by a major-fast-moving-consumer-goods company. • A lexicographic model is formulated for smoothing the supply chain variations. • We consider 7 objective functions for covering all 3 echelons in an integrated way. • Heuristics, exact methods + their combinations are designed, assessed by simulation. • Experiments are performed for instances generated with the company.
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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 | 0.002 | 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 | 0.001 | 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".