Clustering-based resilience in urban quick-commerce last-mile delivery: a simulation study under hub capacity disruptions

Quick-commerce puts significant stress on last-mile delivery systems in dense urban environments, where disruptions such as partial hub failures, localised congestion, or workforce shortages can reduce service reliability. This study develops a simulation-based framework to examine the resilience of clustering-based customer–hub assignment strategies under urban disruption conditions using a realistic road network representation of Kolkata and behaviourally grounded synthetic demand. Fifteen widely used clustering techniques are evaluated across multiple disruption scenarios under flexible and rigid reassignment policies. System performance is assessed using On-Time Delivery (OTD) and Fill Rate, enabling resilience to be characterised in terms of performance stability and degradation behaviour. The analysis reveals pronounced heterogeneity in resilience across clustering approaches. Soft and medoid-based methods which include Fuzzy C-Means and K-Medoids achieved their best resilience performance by maintaining OTD rates between 84–86% and Fill Rates over 92% during conditions of moderate disruption. Under severe hub-capacity disruptions, these methods preserve 8–14% higher OTD and 6–11% higher Fill Rates compared with centroid-based approaches. The results demonstrate that clustering logic is a structural determinant of last-mile resilience and provide a scalable decision-support framework for designing disruption-tolerant quick-commerce delivery networks.