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 observations, fleet-level value learning, and structural feasibility masking. Evaluation on a Los Angeles road-network simulation used chronological data splits, ten random seeds, matched routing baselines, and compound-disruption stress tests. MAPPO served 98.4% of demand versus 96.6% for static OR-Tools and reduced failed packages by 52.2%; under severe compound disruption, service increased from 89.4% to 93.0%. Higher-service adaptive methods reached 99.6–99.7%, with summed online computation times of 0.111–0.503 s per episode compared with 0.029 s for MAPPO. The integrated framework therefore improves disruption recovery over static planning while exposing a clear quality–computation trade-off against adaptive search.
Background: Last-mile delivery couples demand forecasting, heterogeneous fleet allocation, and routing under time windows, yet these decisions are usually studied in isolation. The object of this study is the forecast-to-route decision pipeline of an urban parcel operator; we examine the hypothesis that no single routi...
Metro systems are prone to unplanned disruptions that can trigger cascading delays across the network, particularly when transfer stations are involved. Existing rescheduling approaches often treat the disrupted line in isolation or rely on indirect operational metrics (e.g., train delay) that fail to accurately refl...
Zi-Lu Chen, Xuan Li, Wei Zhu· Journal of Transportation En...· 0 citations
This study develops a discrete-event simulation (DES) of a prospective metro-enabled last-mile delivery system integrating warehouse dispatch, rail transport, and parcel locker distribution. The model represents the full parcel flow from peripheral warehouses to metro depots, train-based transfer, and station lockers u...
Zisis Maleas, Georgia Ayfantopoulou, Sofoklis Dais et al.· European Transport Research...· 0 citations
Overnight rebalancing in dock-based bike-sharing systems requires routing a limited fleet of trucks before user activity begins. This article formulates static rebalancing problem under stochastic demand uncertainty as a bi-objective combinatorial optimization problem that selects truck routes and visited stations. The...
D. Pedroza-Perez, Gabriel Luque, S. Nesmachnow et al.· Journal of combinatorial opt...· 0 citations
Efficient routing optimization is essential to freight transportation, urban logistics, and shared mobility, where high-quality heuristics are often required under limited computational budgets. Recent large language model (LLM)-based automated heuristic design methods can generate effective routing rules, but aggregat...
Bing-Hao Ji, Di Huang, Jia-Hui Fang et al.· 0 citations
DNSGA-II-ALNS is presented, an epoch-based dynamic multi-objective evolutionary algorithm that couples an event-conditioned warm-start projection with an exact marginal assignment cost, feasibility-aware destroy-and-repair search and NSGA-II selection, and achieves comparable front quality at an order of magnitude less...
Jia-Hao Tian, Min Zou, Zhi-Fei Li et al.· Applied Sciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
