Route Optimization for Multi-trip Urban Waste Collection using Vehicle Routing Algorithm: A Case Study in Thimphu City

Abstract

Solid waste management (SWM) is a critical challenge for urban sustainability, influenced by factors such as economic development, technological advancement, and regulatory frameworks. These factors have pushed the management of municipal solid waste into crisis, especially in developing countries including Bhutan. Thimphu, the capitalcity of Bhutan, faces significant SWM issues due to rapid urbanization and population growth. This research aims to optimize the solid waste collection system in Thimphu City through the implementation of the Vehicle Routing Problem (VRP) algorithm and the development of a simple web-based visual interface. The study involves identifying optimal locations for Garbage Accumulation Points (GAPs) and developing an optimized routing strategy for waste collection vehicles. The study considers a truck scheduling problem to effectively distribute collection vehicles in Thimphu City. For this study, a geographic information systems (GIS) based approach consisting of the spatial data analysis, network analysis, and VRP algorithm is performed using Python
3.12. The algorithm considers constraints like vehicle capacity, service time, working hours, and break periods to ensure feasible and efficient routing. For multi-trip solid waste collection in Thimphu city, our algorithm resulted in the deployment of a total of six trucks with different capacities. The simple analysis from the study shows the total time taken for waste collection in North and South Thimphu, is 35.5 hrs and 39.5 hrs with total distances of 202.76 km and 216.93 km respectively. The results demon strate that managing waste collection in subdivided areas (North and South Thimphu) is more efficient than integrating the entire city, highlighting the complexity of city-wide SWM. The optimized routes show significant reductions in travel time and distance, underscoring the potential of advanced algorithms in enhancing operational efficiency.