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In most Swiss municipalities, a curbside system consisting of heavy trucksstopping at almost each household is used for non-recoverable waste collection.Due to the many stops of the trucks, this strategy causes high fuelconsumption, emissions and noise. These effects can be alleviated by reducingthe number of stops performed by collection vehicles. One possibility consistsof locating collection points throughout the municipality such that residentsbring their waste to their most preferred location. The optimization problemconsists of selecting a subset of candidate locations to place the points suchthat each household disposes the waste at the most preferred location. Providedthat the underlying road network is available, we refer to this optimizationproblem as the capacitated multi-vehicle covering tour problem on a roadnetwork (Cm-CTP-R). We introduce two mixed-integer linear programming (MILP)formulations: a road-network-based formulation that exploits the sparsity ofthe network and a customer-based formulation typically used in vehicle routingproblems (VRP). To solve large instances, we propose a two-phased heuristicapproach that addresses the two subproblems the Cm-CTP-R is built on: a setcovering problem to select the locations and a split-delivery VRP to determinethe routes. Computational experiments on both small and real-life instancesshow that the road-network-based formulation is better suited. Furthermore, theproposed heuristic provides good solutions with optimality gaps below 0.5% and3.5% for 75% of the small and real-life instances respectively and is able tofind better solutions than the exact method for many real-life instances.

A capacitated multi-vehicle covering tour problem on a road network and its application to waste collection