A location-production-routing problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology
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Bibliographic record
Abstract
Additive Manufacturing (AM) enhances the flexibility of manufacturing networks. In this paper, we present a Location-Production-Routing (LPR) problem designed for a distributed manufacturing platform, where the manufacturing facilities are distributed in different locations with the support of AM technologies . The proposed LPR problem encompasses three different types of decisions: location-allocation, production planning, and product delivery routing decisions. This is one of the first studies that analyzes integrated logistics and manufacturing optimization under distributed and resilient manufacturing platforms. To efficiently solve the complex problem, we design a novel solution method called the Neural Genetic Algorithm (NGA). The numerical experiments show that the proposed method can attain near-optimal solutions, achieving an average gap of 3% with a standard deviation of 1.4% and a 99% improvement in computational time compared to the CPLEX solver. The sensitivity analysis illustrates the high impact of the unit shortage cost on the customer service level and on the distribution of the AM facilities. Moreover, our results for a given instance show that through the periodic reconfiguration of AM supply chains using the proposed LPR model, we can achieve an average cost reduction of up to 25% in the supply network.
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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 | 0.001 |
| 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