Fuzzy multimodal vehicle routing for last-mile flood relief: A scenario-based model for Chiang Mai

Abstract

This research addresses the logistical challenges of last-mile relief delivery during flood disasters in Chiang Mai, Thailand. We propose a scenario-based fuzzy multi-depot, multi-trip vehicle routing problem model with split deliveries and integrated multimodal accessibility across varying flood severity levels. The model incorporates fuzzy logic to handle uncertainty by transforming it into an equivalent auxiliary crisp model through triangular membership functions. The objective function aims to minimize total transportation time across five flood scenarios. Results demonstrate that community accessibility requirements vary significantly with flood severity: low water levels can be served entirely by trucks, while severe flooding necessitates intensive boat operations, significantly increasing response times and trip frequencies. Sensitivity analysis reveals that strategic boat-drone collaboration in Scenario 4 can reduce the number of trips by 2 and travel time by up to 3.7%, while significantly simplifying boat routing complexity. The study provides actionable insights for disaster management agencies, emphasizing the critical role of scenario-based planning, multimodal fleet deployment, and robust decision-making under uncertainty.