TY - BOOK
T1 - Inventory routing for dynamic waste collection
AU - Mes, Martijn R.K.
AU - Schutten, Johannes M.J.
AU - Perez Rivera, Arturo Eduardo
PY - 2013
Y1 - 2013
N2 - 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 e ects, 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.
AB - 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 e ects, 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.
KW - IR-89427
KW - METIS-302437
M3 - Report
T3 - BETA working paper
BT - Inventory routing for dynamic waste collection
PB - University of Twente
CY - Enschede, the Netherlands
ER -