On the value of shipment consolidation and machine learning techniques for the optimal design of a multimodal logistics network
Bibliographic record
Abstract
We study a multimodal logistics network for a multi-echelon supply chain (SC) with multiple products, considering economic and environmental sustainability and shipment consolidation (ShC). The SC logistics network is modelled as a Mixed Integer Linear Program (MILP) and then tested on randomly generated but realistic test instances. The effects of ShC in SC network design on economic and environmental costs are analyzed, showing that consolidation decreases the SC cost, especially when the distance between the shipper and receiver is significant. Moreover, machine learning (ML) approaches for predicting stochastic parameters using historical data are evaluated compared to the more traditional stochastic programming approaches over multiple prediction periods. The three ML models utilized; namely, Attention CNN-LSTM, Attention ConvLSTM and an ensemble of both models using Support Vector Regression, performed significantly better than the stochastic programming approaches considered (simple recourse and chance-constrained) in all scenarios. The numerical examples show that the MILP models using the predictions from the ML algorithms provide the highest value of the stochastic solution and the lowest expected value of perfect information. This study makes a case for the continued integration of ML prediction methodologies into stochastic optimization modelling in the setting of sustainable SC logistics design problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".