Urban logistics in metropolitan areas faces mounting pressure to deliver faster while controlling operational costs under strict fleet size constraints. Traditional vehicle routing models assume unlimited vehicle availability, overlooking realistic fleet utilization and spatial-temporal demand imbalances. This paper introduces the fleet-constrained metropolitan logistics problem (FCMLP), a novel framework integrating trunk linehaul scheduling, two-echelon routing, multi-trip operations, and anticipatory fleet positioning. We model the FCMLP as a Markov Decision Process capturing the stochastic and dynamic nature of metropolitan delivery flows. Our solution framework combines interpretable Bayesian Network-based demand forecasting for transparent proactive vehicle relocation decisions, parameterized cost-function approximation for dynamic order-to-linehaul assignment, and Adaptive Large Neighborhood Search for multi-trip vehicle routing. Computational experiments on synthetic instances and real-world data from a major e-commerce platform in Jakarta demonstrate 20–26% total cost reduction. Multi-trip operations alone reduce fleet size by 23%, while interpretable predictive relocation further improves performance by 7% through a 20% reduction in emergency deployments. The framework’s interpretability enhances operator trust and facilitates practical adoption, offering logistics platforms a path to improve vehicle utilization through operational efficiency and transparent predictive intelligence without expanding fleet size.

Geopolitical risks—including export controls, entity listings, and end-use restrictions—have become a major source of disruptions in semiconductor supply chains. The impact of such disruptions depends not only on the policy trigger itself but also on the vulnerability of cross-regional partnerships between supply chain partners. Specifically, under the same policy regime, firms with weak partnerships suffer far greater disruption than those with strong partnerships. Apart from risk propagation, this vulnerability also propagates through the supply chain: when an upstream supply channel has weak partnerships, its downstream stages also become more exposed to disruptions. We call this phenomenon vulnerability propagation. Existing Bayesian Network (BN) frameworks portray risk propagation through fixed parameters that do not reflect partnership vulnerability and cannot capture vulnerability propagation. To fill this gap, we propose a Dependency-Robust Bayesian Network (DeRBN) that conditions risk propagation parameters on the partnership vulnerability. A robust worst-case oriented evaluation method is developed to assess the disruption risk under data scarcity. Computational experiments on a typical semiconductor supply chain network show that (i) moving from all-strong to all-weak partnerships increases the worst-case risk by approximately 24%, (ii) the dependency-induced risk amplification is unevenly distributed across supply channels, with the most influential channel contributing approximately 2.2 times the marginal risk of the least influential one, and (iii) the relative ranking of vulnerability profiles remains perfectly stable under varying levels of data uncertainty. These results suggest that DeRBN has the potential to serve not only as a risk assessment tool but also as a diagnostic instrument for identifying and prioritizing the most vulnerable supply channels for targeted risk mitigation.