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Stochastic approaches for product recovery network design: a case study

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  • Listes, O.L.
  • Dekker, R.

Abstract

Increased uncertainty is one of the characteristics of product recovery networks. In particular the strategic design of their logistic infrastructure has to take uncertain information into account. In this paper we present stochastic programming based approaches by which a deterministic location model for product recovery network design may be extended to explicitly account for the uncertainties. Such a stochastic model seeks a solution which is appropriately balanced between some alternative scenarios identified by field experts. We apply the stochastic models to a representative real case study on recycling sand from demolition waste in The Netherlands. The interpretation of the results is meant to give more insight into decision-making for reverse logistics.

Suggested Citation

  • Listes, O.L. & Dekker, R., 2001. "Stochastic approaches for product recovery network design: a case study," Econometric Institute Research Papers EI 2001-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1672
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    References listed on IDEAS

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    1. Barros, A. I. & Dekker, R. & Scholten, V., 1998. "A two-level network for recycling sand: A case study," European Journal of Operational Research, Elsevier, vol. 110(2), pages 199-214, October.
    2. Gilbert Laporte & François V. Louveaux & Luc van Hamme, 1994. "Exact Solution to a Location Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 28(2), pages 95-103, May.
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    1. Choudhary, Alok & Sarkar, Sagar & Settur, Srikar & Tiwari, M.K., 2015. "A carbon market sensitive optimization model for integrated forward–reverse logistics," International Journal of Production Economics, Elsevier, vol. 164(C), pages 433-444.
    2. Wang, Ziping & Yao, Dong-Qing & Huang, Peiqing, 2007. "A new location-inventory policy with reverse logistics applied to B2C e-markets of China," International Journal of Production Economics, Elsevier, vol. 107(2), pages 350-363, June.
    3. de Brito, M.P. & Flapper, S.D.P. & Dekker, R., 2002. "Reverse logistics," Econometric Institute Research Papers EI 2002-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Drexl, Andreas & Klose, Andreas, 2001. "Facility location models for distribution system design," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 546, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    5. Diabat, Ali & Kannan, Devika & Kaliyan, Mathiyazhagan & Svetinovic, Davor, 2013. "An optimization model for product returns using genetic algorithms and artificial immune system," Resources, Conservation & Recycling, Elsevier, vol. 74(C), pages 156-169.
    6. Salema, Maria Isabel Gomes & Barbosa-Povoa, Ana Paula & Novais, Augusto Q., 2007. "An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1063-1077, June.
    7. Fleischmann, M., 2001. "Reverse Logistics Network Structures and Design," ERIM Report Series Research in Management ERS-2001-52-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Listes, O.L., 2002. "A decomposition approach to a stochastic model for supply-and-return network design," Econometric Institute Research Papers EI 2002-43, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Klose, Andreas & Drexl, Andreas, 2005. "Facility location models for distribution system design," European Journal of Operational Research, Elsevier, vol. 162(1), pages 4-29, April.

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