Inventory routing for dynamic waste collection

Martijn R.K. Mes, Johannes M.J. Schutten, Arturo Eduardo Perez Rivera

    Research output: Book/Report › Report › Professional

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    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 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.
    Original languageEnglish
    Place of PublicationEnschede, the Netherlands
    PublisherUniversity of Twente
    Publication statusPublished - 2013

    Publication series

    NameBETA working paper
    PublisherUniversity of Twente
    No.431
    Volume431

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    Containers
    Sensors
    Costs
    Industry
    Experiments
    Uncertainty

    Keywords

    • IR-89427
    • METIS-302437

    Cite this

    Mes, M. R. K., Schutten, J. M. J., & Perez Rivera, A. E. (2013). Inventory routing for dynamic waste collection. (BETA working paper; Vol. 431, No. 431). Enschede, the Netherlands: University of Twente.
    Mes, Martijn R.K. ; Schutten, Johannes M.J. ; Perez Rivera, Arturo Eduardo. / Inventory routing for dynamic waste collection. Enschede, the Netherlands : University of Twente, 2013. (BETA working paper; 431).
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    Mes, MRK, Schutten, JMJ & Perez Rivera, AE 2013, Inventory routing for dynamic waste collection. BETA working paper, no. 431, vol. 431, University of Twente, Enschede, the Netherlands.

    Inventory routing for dynamic waste collection. / Mes, Martijn R.K.; Schutten, Johannes M.J.; Perez Rivera, Arturo Eduardo.

    Enschede, the Netherlands : University of Twente, 2013. (BETA working paper; Vol. 431, No. 431).

    Research output: Book/Report › Report › Professional

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    T1 - Inventory routing for dynamic waste collection

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    AU - Schutten, Johannes M.J.

    AU - Perez Rivera, Arturo Eduardo

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    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.

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    Mes MRK, Schutten JMJ, Perez Rivera AE. Inventory routing for dynamic waste collection. Enschede, the Netherlands: University of Twente, 2013. (BETA working paper; 431).

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