With the advent of IoT technologies, it is becoming increasingly feasible to significantly improve the solutions of one of the most common combinatorial optimization problems—the Capacitated Vehicle Routing Problem (CVRP). CVRP is a cornerstone in virtually every logistical task. In this study, we integrate a novel technology, "smart bins", with established optimization algorithms. We collected GIS data on smart bins across a substantial region of Slovenia to construct asymmetric directed graphs, which model a potential "smart city". This approach allows for a more realistic representation of urban routing challenges, reflecting the one-way streets and varied traffic conditions typical in urban environments. Our case study focuses on a weekly recurring bin collection task, with the bin filling speeds deviating by up to approximately 1.5 days from the mean. This periodic nature of the task adds a layer of complexity to the optimization problem. Our results show a reduction in the costs of the current optimum by around 6.3%. Properly utilized, this technology can yield significant cost and time savings while reducing ecological impact.
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