arbiter
JournalsAccount

Hybrid MILP-deep reinforcement learning approach for reusable container flows in the automotive industry

Eduardo Guzmán et al.

International Journal of Production Economics2026https://doi.org/10.1016/j.ijpe.2026.109927article
AJG 3ABDC A
Weight
0.50

Abstract

No abstract available.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.ijpe.2026.109927

Or copy a formatted citation

@article{eduardo2026,
  title        = {{Hybrid MILP-deep reinforcement learning approach for reusable container flows in the automotive industry}},
  author       = {Eduardo Guzmán et al.},
  journal      = {International Journal of Production Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ijpe.2026.109927},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Hybrid MILP-deep reinforcement learning approach for reusable container flows in the automotive industry

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.