Sustainable urban waste collection using a hybrid heuristic–genetic approach: a Bangkok case study

Abstract

Urban waste collection is a critical component of sustainable city development, directly influencing emissions reduction, resource efficiency, and public health. This study develops a hybrid optimization framework combining a Nearest Neighbor Heuristic with a Genetic Algorithm (GA) to optimize municipal waste collection routes in Bangkok, addressing the Vehicle Routing Problem (VRP) under real-world constraints such as vehicle capacity, time windows, and traffic conditions. The optimized algorithm reduced weekly travel distance by 8.51% and increased average vehicle utilization by 7.78%, translating into projected five-year economic benefits of over 4.7 million Baht and annual GHG emission reduction equivalent to planting approximately 1,750 trees. These findings demonstrate how algorithmic optimization can advance SDG 11 (sustainable cities and communities) and SDG 12 (responsible consumption and production) by aligning technical innovation with environmental and social outcomes. Beyond Bangkok, the framework is scalable to other rapidly urbanizing contexts, offering policymakers a data-driven pathway toward inclusive, low-carbon, and effective waste management systems.