Digital tvilling inom sophantering

Abstract

In connection with increasing urbanization and growing demands for sustainable resource utilization, higher requirements are being placed on the optimization of systems in urban environments. A central aspect of all urban environments is waste management, and this study investigates how digital twins can be used to improve waste management in Gothenburg Municipality. The project was carried out on behalf of Volvo Group Trucks and aims to analyze how data integration between stakeholders can enable improved route planning and create new business opportunities. The study is based on a design science approach with an iterative working method, where interviews with employees from Volvo Group Trucks, Renova, and the City of Gothenburg were conducted. The information from these interviews is combined with literature studies, service blueprints, and the development of a simplified simulation model. The simulation compares the current state with a demand-driven model based on the fill levels of bins, which could potentially be measured using sensors. The results demonstrate that there is room for efficiency improvements through increased data sharing and dynamic route optimization. The simulation model shows how demand-driven collection can reduce unnecessary emptying operations and help prevent overflowing bins. The economic analysis also indicates potential cost savings, although assumptions have been made and the results should therefore be interpreted with some caution. Furthermore, the recommendations suggest a phased implementation in which the initial focus should be on utilizing existing data and establishing simpler forms of data-driven route planning before more advanced functions are implemented. In the longer term, a solution integrating realtime data, sensors, and optimization algorithms can be developed. However, the results also show that successful implementation requires not only technical solutions but also collaboration between stakeholders and access to relevant shareable data.