title: Inventory routing for dynamic waste collection.
abstract: 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.
authors.affiliation: Department Industrial Engineering and Business Information Systems, School of Management and Governance, University of Twente, The Netherlands. Electronic address: m.r.k.mes@utwente.nl.
authors.forename: Martijn
authors.initials: M
authors.lastname: Mes
authors.affiliation: Department Industrial Engineering and Business Information Systems, School of Management and Governance, University of Twente, The Netherlands.
authors.forename: Marco
authors.initials: M
authors.lastname: Schutten
authors.affiliation: Department Industrial Engineering and Business Information Systems, School of Management and Governance, University of Twente, The Netherlands.
authors.forename: Arturo Pérez
authors.initials: AP
authors.lastname: Rivera
mesh_terms: D000465:Algorithms; D014186:Transportation; D018505:Waste Management
keywords: Inventory routing; Optimal learning; Simulation optimization; Transportation; Waste collection