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A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty — arXiv:2608.10245

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10245
License (as archived): CC0-1.0
Archived: 2026-10-19T06:19:42.823Z
SHA-256 of the archived text: 010d10e9437ebf23339b534d3e78d4ea597136bbd5fe39e9912e320f42b6cfc4
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How to cite

A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty — arXiv:2608.10245 — arXiv (en), https://arxiv.org/abs/2608.10245 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df1638ddcefa (Metraipolis archive dc_df1638ddcefa, sha256 010d10e9437ebf23339b534d3e78d4ea597136bbd5fe39e9912e320f42b6cfc4)

BibTeX

@misc{dcdf1638ddcefa,
  title = {A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty — arXiv:2608.10245},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10245},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df1638ddcefa; sha256 010d10e9437ebf23339b534d3e78d4ea597136bbd5fe39e9912e320f42b6cfc4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10245. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment among collaborative hubs, retailer deliveries, and shortages. The problem combines discrete assignment decisions with interdependent continuous flows, while uncertain operating costs make robust planning more difficult. This study formulates deterministic and min-max regret models for a three-echelon network of factories, hubs, and retailers and develops a graph neural network-guided genetic algorithm (GNN-GA) for the assignment decisions. The GNN estimates hub-specific factory-selection probabilities that are used to construct the initial GA population and adapt mutation according to prediction uncertainty. Each previously unseen candidate assignment is evaluated by solving the remaining continuous-flow problem to LP optimality. Simulated annealing, a standard GA, and GNN-GA are compared on 15 instances using matched random seeds and fixed limits on distinct assignment evaluations. Because the evaluation budgets for test Instances 13-15 are smaller than the nominal population size, these experiments primarily assess the quality of learned initialization rather than multi-generation evolutionary search. A separate 400-evaluation experiment on exact test Instance 13 permits three complete offspring generations and a partial fourth pass, with GNN-GA outperforming GA in all 10 matched runs. Three independently generated exact-solvable instances provide a separate test of transfer. Ablation results show that learned initialization provides most of the improvement, while entropy-guided mutation has a smaller, instance-dependent effect. Per-instance solution times include GNN inference and search but exclude model training and one-time model setup.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.LG).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.10245 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:11:45.127Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).