Nowadays smart waste management is an integral part of Smart Cities (Schaffers et al., 2011; Anagnostopoulos et al., 2017). In October 2013, an article published by the (World Bank, 2013) states that ‘By 2100,…, the growing global urban population will be producing three times as much waste as it does today’. This means that the need for an efficient waste management system is exceptionally high. For example, current practice of garbage collection in Melbourne, Australia (www.melbourne.vic.gov.au, 2017) requires the municipality to hire special workers, i.e. garbage truck drivers, for collecting garbage, and they still need to go through all the streets where the bins are located on a weekly basis, and receive the same salary regardless of the garbage bins in their area being full or partially empty. This creates extra cost to the municipality and produces additional air pollution as a result of unnecessary driving. This research aims to improve environmental sustainability using sensor-based technology in waste management. The goal of the research is to facilitate information exchange between citizens, dispatchers of the garbage truck fleet and drivers themselves by employing modern sensor technology and information system infrastructure for decision support. This paper describes an innovative decision support system (DSS) architecture to support this process as an artefact of the design science research. This DSS is intended to aid a dispatcher in solving the optimal route problem for garbage collection based on the capacity and type of the trucks, and the start and end locations of the path. Using sensor technology, system collects all the notifications from the bins needed to be emptied, and shows them on the dispatcher’s map, proposing the route to follow for garbage collection. We also consider a different type of vehicles designed to serve the needs of the citizens. Since the research goal is to find an optimal path for a particular truck, this is a modified heterogeneous vehicle routing problem (VRP), that is a modification of the classical VRP, which is capable to consider different garbage truck capacities.