Inventory routing for dynamic waste collection

• We propose a fast parameterized heuristic for solving the IRP with many customers.
• We use optimal learning techniques combined with simulation to tune the parameters.
• The approach is illustrated using a case study at a waste collection company.
• The policies are able to produce near-optimal parameter settings in limited time.
• The policies are able to realize costs reductions up to 40% for our case study.

We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal effects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters.