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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Mohamed-Ali Ejjanfi

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2 papers indexed here

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Open access Sep 2026

A Digital-Twin-Oriented Framework for Candidate-Locker Demand and Road-Risk Prediction

Last-mile planning benefits from two complementary predictive signals before routing: expected package demand at candidate consolidation nodes and area-level road risk. This study develops a retrospective, digital-twin-oriented framework for a Los Angeles case study. It aligns delivery, weather, map, and collision reco...

Mohamed-Ali Ejjanfi, Kadim Lahcen Nadime, J. Benhra · 0 citations
Open access Sep 2026

Solver-Independent MAPPO for Disruption-Aware Last-Mile Routing via Candidate Parcel-Locker Consolidation Points

Urban last-mile routes must adapt when demand and network conditions change after dispatch. This study proposes a solver-independent multi-agent proximal policy optimization (MAPPO) framework that combines a shared fleet policy, dynamically ranked candidate parcel-locker consolidation points, disruption-aware observati...

Mohamed-Ali Ejjanfi, Kadim Lahcen Nadime, J. Benhra · 0 citations

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