A data-driven approach to optimal sensor placement for waste collection

Background: Solid waste collection is a relevant issue for municipalities and can be improved by installing volumetric sensors inside dumpsters. Sensors generate a maintenance cost but provide additional information to decide which dumpsters to empty in a given day when visiting all of them is expensive. Moreover, dumpsters close to each other are expected to follow similar filling trends, as they serve the same catchment area; hence, equipping them all with sensors may be inconvenient. This leads to the problem of finding sensor locations that minimize routing, waste overflow, and sensor maintenance costs. Methods: We tackle the problem using a heuristic based on adaptive large neighborhood search and a one-step look-ahead policy, performed through a rolling horizon method to approximate the multi-stage stochastic programming problem, in order to compute the number and locations of sensors to be installed, minimizing the total cost. Results: We apply the proposed approach to a realistic setting with 50 dumpsters in Torino. The results show that placing sensors in 21 dumpsters at optimized locations allowed saving about 17,000 € per year and reduced vehicle emissions by 15.5%. Conclusions: The proposed approach enables more cost-effective and sustainable waste collection operations.