Integrating Hamiltonian circuits with AHP for optimal route planning

Urban transportation systems face increasing challenges due to traffic congestion, travel delays, and operational inefficiencies, particularly in municipal solid waste collection services. Traditional route planning methods, including shortest-path algorithms such as Dijkstra’s algorithm, primarily optimize a single factor such as travel distance or travel time, which may not adequately represent the complex traffic and infrastructure conditions encountered in urban environments. Despite advances in route optimization, limited research has integrated coverage-based routing with multi-criteria evaluation within a unified framework. To address this gap, this study proposes an integrated route optimization framework that combines the Hamiltonian Circuit concept with the Analytic Hierarchy Process (AHP). The Hamiltonian Circuit ensures complete coverage of waste collection locations without duplication, while AHP evaluates alternative routes using multiple criteria, including travel time, traffic volume, distance, road width, parking effects, encroachment, and intersection density. The framework is validated using a municipal waste collection network in Vellore, Tamil Nadu, India. The results identify Route 3 as the most efficient alternative, providing the best overall balance among operational and transportation-related criteria. Compared with conventional single-factor approaches based solely on distance or travel time, the proposed framework offers a more comprehensive assessment of route performance. The study demonstrates that integrating graph-based routing with multi-criteria decision-making (MCDM) can support more effective municipal service planning and assist urban authorities in improving operational efficiency and sustainable transportation management.