GIS-based optimization of solid waste collection points and routes: A case study of Majengo Ward, Sumbawanga Municipality, Tanzania

Abstract

This study investigates the potential of Geographic Information Systems (GIS) and optimization techniques to improve municipal solid waste management in Sumbawanga Municipality, Tanzania. While GIS applications in waste management are established, scalable models for secondary cities in resource-limited African contexts remain understudied. The research focuses on Majengo Ward, a rapidly urbanizing area with an inadequate waste collection system. Through a mixed-methods approach, the study bridges this gap by proposing solutions to enhance waste collection coverage, reduce environmental impact, and optimize operational efficiency. Spatial analysis reveals significant inefficiencies in the existing system, with only 40 % of the 3.5 tonnes of daily waste collected. Using GIS and suitability modeling, seven new collection points are strategically identified, considering population density, road proximity, and environmental risks. Optimization algorithms (p-median and p-center) design efficient routes, minimizing distances and travel times. The proposed system is projected to increase waste collection coverage by 90 %, reduce fuel consumption by 25 %, and decrease driver working hours by 30 %. The cost-benefit analysis demonstrates significant economic and environmental benefits, with a payback period of 3.5 years. This study highlights the effectiveness of data-driven approaches in addressing waste management challenges in developing cities. The findings offer valuable insights for policymakers, planners, and waste management practitioners, contributing to sustainable urban development in Tanzania and similar contexts. • GIS-based approach optimized waste collection routes, increasing coverage by 90 %. • Seven new collection points identified, reducing fuel consumption by 25 %. • Daily waste generation of 3.5 tonnes primarily consists of polythene, food scraps, and paper. • P-median and p-center algorithms improved route efficiency, cutting driver hours by 30 %. • This study offers a scalable model for waste management in rapidly urbanizing regions.