Nowadays, disruptive events increasingly affect supply chain resilience and performance, while limited raw material availability drives global adoption of the Circular Economy paradigm. This study assesses how Circular Economy practices influence supply chain resilience by investigating the joint effect of structural design and operational control parameters through simulation-based analysis integrated with Design of Experiments. We examine five sourcing configurations, namely single-sourcing, double-sourcing, symbiotic-sourcing, closed-loop and circular-sourcing. Results show that structural design emerges as the primary determinant of resilience, shaping how supply chains respond to disruptions. At the same time, supply chain resilience is a dynamic outcome that depends on the alignment of sourcing strategies with operational policies. Overall, the adoption of Circular Economy-based sourcing strengthens absorptive capacity, to the extent that service continuity can be maintained without relying on high safety factors.

This paper presents the Probabilistic Topology-Aware Multi-Modal K-Nearest Neighbor (P-TAM-KNN) framework for assessing the resilience of California's Logging Truck Network (LTN). Two integrated contributions are made. First, Weibull reliability theory is embedded inside the KNN mechanism: the k structurally nearest bridge analogues identified by the Heterogeneous Network-Topology Distance (HNTD) are used as a distance-weighted sample for Weibull MLE, transforming every KNN query into a time-varying failure probability — the first use of KNN structural analogues as a Weibull parameter estimator. Second, the Supply Chain Resilience Index (SCRI) integrates the time-varying Weibull failure probability with corridor-level disruption severity and recovery capacity into a single risk metric, identifying US-101 as the most resilience-critical LTN corridor. Two-stream validation (KNN-Weibull parameters and Caltrans operational maintenance records) independently converges on US-101 as the highest-priority corridor for intervention.

Disrupted humanitarian supply chains require decisions that are fast, interpretable, and robust to operational deterioration. This paper develops a policy stress-testing framework for relief-network planning under disruption by transforming multi-objective optimisation outputs into policy classes that can be compared under different operating scales. A scenario-based humanitarian supply chain model is solved using a preference-guided extension of NSGA-II, and the resulting non-dominated solutions are translated into lean, balanced, and resilient policy classes. Three test cases are considered, representing local, regional, and corridor-level relief operations. The numerical study shows that preference-guided search consistently identifies policies with better service continuity and lower resilience loss than classical baselines, while the graphical stress-testing analysis clarifies the trade-off between additional cost and service protection. The study demonstrates how disruption data can be converted into policy-relevant insight for humanitarian logistics planning.