6. Conclusions and future work
The research presented in this paper focuses on the usage of DSS for Sustainable Development. Gholami et al. (2016) highlighted the lack of practical ‘impact-oriented’ solutions for the ill-structured questions of Green ICT and Sustainability, and the current project aims to fulfil this gap. As the example use case, the smart waste management case was chosen. The idea of the project was to create a decision support tool for the coordinators that allows the drivers of the garbage trucks to choose the optimal path for collecting garbage. It was also shown with the help of different scenarios that the modern way of garbage collection requires new and innovative solutions, and modifications to the garbage truck was suggested to improve the garbage collection process. By introducing such modifications, we have received more than a 50% reduction in both distance covered and CO2 emission.
The project has a potential for further research. First, the impact of the city size is an opportunity for future investigation. Secondly, the data collected from sensors and then processed by the DSS, can be used by the municipality for the prediction of the garbage bins fill trends. It is obvious that, for example, during the weekend garbage to a higher extent is generated in residential neighbourhoods than on weekdays, and, therefore, they might require their garbage bin to be emptied sooner. At the same time, there’s an opportunity to use the data from DSS as the foundation one for deciding where to build an incinerator and of which type. For example, offices in the city centre usually don’t produce much bio waste, whereas the amount of paper and plastic waste may be significant. It might be reasonable to have the incinerator for plastic and paper somewhere not far from the office part of the city, to reduce the distance HCTs would need to cover to bring the garbage to the incinerator.
